-----------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/replication_log.log
  log type:  text
 opened on:  21 Apr 2023, 11:50:39

. 
. set matsize 8000
set matsize ignored.
    Matrix sizes are no longer limited by c(matsize) in modern Statas.  Matrix sizes are now
    limited by edition of Stata.  See limits for more details.

. 
. 
. * trends data
. 
. import excel "jhu 2020 covid data.xlsx", sheet("Sheet1") firstrow clear
(516 vars, 276 obs)

. collapse (sum) E-SV, by(CountryRegion)

. keep if CountryRegion=="US" | CountryRegion=="United Kingdom" | CountryRegion=="Brazil" | Cou
> ntryRegion=="Italy" | CountryRegion=="China" | CountryRegion=="India"
(187 observations deleted)

. replace CountryRegion="UK" if CountryRegion=="United Kingdom"
(1 real change made)

. 
. local i = 1

. foreach v of var E-SV {
  2.          rename `v' v`i'
  3.                  local i = `i' + 1
  4. }

. reshape long v, i(CountryRegion) j(var)
(j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 
> 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 
> 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 
> 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 1
> 20 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 14
> 3 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166
>  167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 
> 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 2
> 13 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 23
> 6 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259
>  260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 
> 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 3
> 06 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 32
> 9 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
>  353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 
> 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 3
> 99 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 42
> 2 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445
>  446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 
> 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 4
> 92 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512)

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations                6   ->   3,072       
Number of variables                 513   ->   3           
j variable (512 values)                   ->   var
xij variables:
                         v1 v2 ... v512   ->   v
-----------------------------------------------------------------------------

. reshape wide v, i(var) j(CountryRegion) string
(j = Brazil China India Italy UK US)

Data                               Long   ->   Wide
-----------------------------------------------------------------------------
Number of observations            3,072   ->   512         
Number of variables                   3   ->   7           
j variable (6 values)     CountryRegion   ->   (dropped)
xij variables:
                                      v   ->   vBrazil vChina ... vUS
-----------------------------------------------------------------------------

. rename v* *

. rename ar date

. tsset date

Time variable: date, 1 to 512
        Delta: 1 unit

. format date %td

. replace date = date+21935
(512 real changes made)

. 
. 
. foreach v of var Brazil-US {
  2.         replace `v' = `v'/1000
  3.         gen change_`v' = `v'-`v'[_n-1]
  4.         tssmooth ma change_`v'_sm = change_`v', window(2 1 2)
  5. }
(477 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_Brazil
(512 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_China
(504 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_India
(503 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_Italy
(503 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_UK
(512 real changes made)
(1 missing value generated)
The smoother applied was
     (1/5)*[x(t-2) + x(t-1) + 1*x(t) + x(t+1) + x(t+2)]; x(t)= change_US

. 
. 
. 
. twoway (tsline change_Brazil_sm, lpattern(dash)) ///
>         (tsline change_China_sm, lpattern(longdash) lcolor(black)) ///
>         (tsline change_Italy_sm, ) ///
>         (tsline change_UK_sm, lcolor(black)) ///
>         (tsline change_US_sm, lpattern(dot) scheme(tpepbw) ///
>         ttext(20 01feb2020 "China") ///
>         ttext(200 01feb2021 "USA") ///
>         ttext(75 01jul2021 "Brazil") ///
>         ttext(32 30mar2021 "Italy") ///
>         ttext(32 05feb2021 "UK") ///
>         tlabel(01mar2020 01may2020 01jul2020 01sep2020 01nov2020 01jan2021 01mar2021 01may202
> 1 01jul2021, angle(forty_five) format("%tdMon-YY")) ///
>         xtitle("") ytitle("New Cases (1000s)") legend(off))

. graph export "figures/S1.pdf", as(pdf) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/S1.pdf saved as
    PDF format

. graph export "figures/S1.png", as(png) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/S1.png saved as
    PNG format

. 
.  
.  
. use "survey.dta", clear

. 
. gen aia = (iai*-1)+6
(1,352 missing values generated)

. 
. gen feelingthermometer = .
(4,350 missing values generated)

. replace feelingthermometer = (q137_4+q137_5+q137_6)/3 if race==1
(1,248 real changes made)

. replace feelingthermometer = (q137_3+q137_5+q137_6)/3 if race==2
(150 real changes made)

. replace feelingthermometer = (q137_3+q137_4+q137_6)/3 if race==3
(157 real changes made)

. replace feelingthermometer = feelingthermometer*-1
(1,555 real changes made)

. 
. 
. local balance_vars "gender agecat race marstat educ income rural inputstate democrat racialre
> sentment"

. matrix drop _all

. 
. foreach var of local balance_vars {
  2. 
.         
. 
.         
.         qui tabulate `var' entry_exp, chi
  3.         local p = round(r(p),.001)              
  4.         di `"`var' "' `p'
  5.         
.         matrix resmat = nullmat(resmat) \ `p'
  6.                 
. }
gender .283
agecat .568
race .52
marstat .257
educ .409
income .409
rural .237
inputstate .633
democrat .292
racialresentment .584

. 
. matrix rownames resmat = `balance_vars'

. matrix colnames resmat =  Entry

. matrix list resmat

resmat[10,1]
              Entry
      gender   .283
      agecat   .568
        race    .52
     marstat   .257
        educ   .409
      income   .409
       rural   .237
  inputstate   .633
    democrat   .292
racialrese~t   .584

. 
. forvalues i = 3/6 {
  2.         
.         foreach var of local balance_vars {
  3. 
.         qui tabulate `var' banentryexp_w`i', chi
  4.         local p = round(r(p),.001)              
  5.         di `"`var' "' `p'
  6.         
.         matrix resmat_w`i' = nullmat(resmat_w`i') \ `p'
  7.                 
.         }
  8.         
.         matrix list resmat_w`i'
  9. 
. }
gender .954
agecat .553
race .539
marstat .888
educ .899
income .202
rural .639
inputstate .12
democrat .969
racialresentment .13

resmat_w3[10,1]
       c1
 r1  .954
 r2  .553
 r3  .539
 r4  .888
 r5  .899
 r6  .202
 r7  .639
 r8   .12
 r9  .969
r10   .13
gender .057
agecat .408
race .816
marstat .389
educ .451
income .414
rural .095
inputstate .074
democrat .556
racialresentment .264

resmat_w4[10,1]
       c1
 r1  .057
 r2  .408
 r3  .816
 r4  .389
 r5  .451
 r6  .414
 r7  .095
 r8  .074
 r9  .556
r10  .264
gender .909
agecat .43
race .959
marstat .356
educ .017
income .295
rural .669
inputstate .013
democrat .742
racialresentment .553

resmat_w5[10,1]
       c1
 r1  .909
 r2   .43
 r3  .959
 r4  .356
 r5  .017
 r6  .295
 r7  .669
 r8  .013
 r9  .742
r10  .553
gender .076
agecat .729
race .284
marstat .041
educ .749
income .211
rural .868
inputstate .112
democrat .867
racialresentment .216

resmat_w6[10,1]
       c1
 r1  .076
 r2  .729
 r3  .284
 r4  .041
 r5  .749
 r6  .211
 r7  .868
 r8  .112
 r9  .867
r10  .216

. 
. 
. 
. putexcel set "tables/S1.xlsx", sheet("sheet1") replace
Note: File will be replaced when the first putexcel command is issued.

. putexcel A1=matrix(resmat), names
file tables/S1.xlsx saved

. putexcel C2=matrix(resmat_w3)
file tables/S1.xlsx saved

. putexcel D2=matrix(resmat_w4)
file tables/S1.xlsx saved

. putexcel E2=matrix(resmat_w5)
file tables/S1.xlsx saved

. putexcel F2=matrix(resmat_w6)
file tables/S1.xlsx saved

. 
. cd "${pathname}/tables"
/Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/tables

. asdoc sum gender agecat race marstat educ income  rural democrat racialresentment if weight_w
> 1!=., save(S9.doc) replace

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      gender |      3,000       1.532    .4990581          1          2
      agecat |      3,000        1.66    .9760725          0          3
        race |      3,000    1.605667    1.229365          1          8
     marstat |      3,000       2.796    1.880309          1          6
        educ |      3,000       3.528    1.472166          1          6
-------------+---------------------------------------------------------
      income |      3,000    7.754667    4.831659          1         17
       rural |      2,880    2.204861    1.747636          1          9
    democrat |      3,000    1.091333    .7745051          0          2
racialrese~t |      3,000       3.181    1.460006          1          5
Click to Open File:  S9.doc

. asdoc tabulate inputstate if weight_w1!=., save(S9.doc) append

                    inputstate |      Freq.     Percent        Cum.
-------------------------------+-----------------------------------
                       Alabama |         39        1.30        1.30
                        Alaska |          7        0.23        1.53
                       Arizona |         81        2.70        4.23
                      Arkansas |         44        1.47        5.70
                    California |        260        8.67       14.37
                      Colorado |         44        1.47       15.83
                   Connecticut |         31        1.03       16.87
                      Delaware |         14        0.47       17.33
          District of Columbia |          8        0.27       17.60
                       Florida |        224        7.47       25.07
                       Georgia |        101        3.37       28.43
                        Hawaii |         12        0.40       28.83
                         Idaho |         20        0.67       29.50
                      Illinois |        116        3.87       33.37
                       Indiana |         62        2.07       35.43
                          Iowa |         29        0.97       36.40
                        Kansas |         16        0.53       36.93
                      Kentucky |         43        1.43       38.37
                     Louisiana |         41        1.37       39.73
                         Maine |         14        0.47       40.20
                      Maryland |         44        1.47       41.67
                 Massachusetts |         57        1.90       43.57
                      Michigan |        111        3.70       47.27
                     Minnesota |         45        1.50       48.77
                   Mississippi |         28        0.93       49.70
                      Missouri |         67        2.23       51.93
                       Montana |         15        0.50       52.43
                      Nebraska |         13        0.43       52.87
                        Nevada |         45        1.50       54.37
                 New Hampshire |         24        0.80       55.17
                    New Jersey |         67        2.23       57.40
                    New Mexico |         30        1.00       58.40
                      New York |        175        5.83       64.23
                North Carolina |         89        2.97       67.20
                  North Dakota |          8        0.27       67.47
                          Ohio |        111        3.70       71.17
                      Oklahoma |         27        0.90       72.07
                        Oregon |         59        1.97       74.03
                  Pennsylvania |        178        5.93       79.97
                  Rhode Island |          8        0.27       80.23
                South Carolina |         47        1.57       81.80
                  South Dakota |         10        0.33       82.13
                     Tennessee |         52        1.73       83.87
                         Texas |        185        6.17       90.03
                          Utah |         29        0.97       91.00
                       Vermont |         10        0.33       91.33
                      Virginia |         99        3.30       94.63
                    Washington |         72        2.40       97.03
                 West Virginia |         26        0.87       97.90
                     Wisconsin |         60        2.00       99.90
                       Wyoming |          3        0.10      100.00
-------------------------------+-----------------------------------
                         Total |      3,000      100.00
Click to Open File:  S9.doc

. asdoc tabulate democrat racialresentment if weight_w1!=., save(S16.doc) replace

           |                    racialresentment
  democrat |         1          2          3          4          5 |     Total
-----------+-------------------------------------------------------+----------
Republican |        22         33        118        216        386 |       775 
  Democrat |       397        219        294        150        116 |     1,176 
     Other |       187        123        258        202        279 |     1,049 
-----------+-------------------------------------------------------+----------
     Total |       606        375        670        568        781 |     3,000 
Click to Open File:  S16.doc

. cd "${pathname}"
/Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication

. 
. * validate RR measure
. 
. eststo valid: reg feelingthermometer i.racialresentment, robust

Linear regression                               Number of obs     =      1,555
                                                F(4, 1550)        =      14.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0346
                                                Root MSE          =     21.743

----------------------------------------------------------------------------------
                 |               Robust
feelingthermom~r | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
racialresentment |
              2  |   .6646462    1.86365     0.36   0.721    -2.990896    4.320188
              3  |   8.083794   1.718675     4.70   0.000     4.712621    11.45497
              4  |   6.478897    1.72014     3.77   0.000      3.10485    9.852945
              5  |   10.63438   1.672295     6.36   0.000      7.35418    13.91458
                 |
           _cons |  -79.47551   1.240901   -64.05   0.000    -81.90953   -77.04149
----------------------------------------------------------------------------------

. esttab valid using "tables/S3.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S3.csv)

. 
. 
. 
. 
. 
. * WAVE 1 BAN ENTRY EXPERIMENT
. 
. 
. 
. gen entry_exp_recode = .
(4,350 missing values generated)

. replace entry_exp_recode = 1 if entry_exp==2
(1,001 real changes made)

. replace entry_exp_recode = 2 if entry_exp==1
(976 real changes made)

. replace entry_exp_recode = 3 if entry_exp==3
(1,023 real changes made)

. label define entry_exp_recode 1 "Britain" 2 "China" 3 "Italy"

. label values entry_exp_recode entry_exp_recode

. 
. summarize banentry_w1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 banentry_w1 |      2,991    3.860916    1.200075          1          5

. local mean_b = r(mean)

. 
. summarize aia, detail

                             aia
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%     1.333333              1       Obs               2,998
25%            2              1       Sum of wgt.       2,998

50%     2.666667                      Mean           2.725706
                        Largest       Std. dev.      1.085131
75%     3.333333              5
90%     4.333333              5       Variance       1.177509
95%     4.666667              5       Skewness       .1865406
99%            5              5       Kurtosis       2.229429

. local aia_median = r(p50)

. 
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs               3,000
25%            2              1       Sum of wgt.       3,000

50%            3                      Mean              3.181
                        Largest       Std. dev.      1.460006
75%            5              5
90%            5              5       Variance       2.131616
95%            5              5       Skewness      -.2031789
99%            5              5       Kurtosis        1.70661

. local race_median = r(p50)

. 
. 
. 
. * SATE estimates
. eststo sate_w1: reg banentry_w1 i.entry_exp_recode  [pweight=weight], robust
(sum of wgt is 2,992.05180266111)

Linear regression                               Number of obs     =      2,991
                                                F(2, 2988)        =      11.49
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0089
                                                Root MSE          =     1.1818

----------------------------------------------------------------------------------
                 |               Robust
     banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
entry_exp_recode |
          China  |   .1115622   .0583581     1.91   0.056    -.0028639    .2259883
          Italy  |   .2722576   .0571657     4.76   0.000     .1601694    .3843457
                 |
           _cons |   3.753295   .0408908    91.79   0.000     3.673118    3.833472
----------------------------------------------------------------------------------

. eststo sate_w1_c: reg banentry_w1 i.entry_exp_recode  i.agecat gender white married i.educ i.
> income i.inputstate i.rural [pweight=weight], robust
(sum of wgt is 2,875.03568620417)

Linear regression                               Number of obs     =      2,871
                                                F(87, 2783)       =       3.22
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0908
                                                Root MSE          =      1.145

---------------------------------------------------------------------------------------
                      |               Robust
          banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
     entry_exp_recode |
               China  |   .0985599   .0582023     1.69   0.090    -.0155643     .212684
               Italy  |    .283745   .0561676     5.05   0.000     .1736106    .3938794
                      |
               agecat |
                 30-  |   .0952799   .0782512     1.22   0.223    -.0581563    .2487161
                 45-  |   .3507975   .0752612     4.66   0.000     .2032241     .498371
                 65-  |   .3031898    .082258     3.69   0.000      .141897    .4644827
                      |
               gender |   .0939538   .0466296     2.01   0.044     .0025217    .1853859
                white |   .1190346   .0575128     2.07   0.039     .0062625    .2318066
              married |   .0746943    .050914     1.47   0.142    -.0251388    .1745274
                      |
                 educ |
High school graduate  |  -.3681301   .1202475    -3.06   0.002    -.6039135   -.1323467
        Some college  |  -.5800851   .1230224    -4.72   0.000    -.8213096   -.3388606
              2-year  |  -.4089922   .1306579    -3.13   0.002    -.6651883    -.152796
              4-year  |  -.7824549   .1276189    -6.13   0.000    -1.032692   -.5322177
           Post-grad  |  -.8333886   .1373138    -6.07   0.000    -1.102636   -.5641414
                      |
               income |
                   2  |   .0505602   .1313001     0.39   0.700    -.2068952    .3080156
                   3  |   .0794512   .1228284     0.65   0.518    -.1613928    .3202953
                   4  |   -.104908   .1376099    -0.76   0.446    -.3747357    .1649198
                   5  |   .0357278   .1313526     0.27   0.786    -.2218306    .2932862
                   6  |   .1349901   .1379494     0.98   0.328    -.1355033    .4054835
                   7  |   .2307558   .1371449     1.68   0.093    -.0381602    .4996719
                   8  |  -.0336176   .1431274    -0.23   0.814    -.3142643     .247029
                   9  |   .0070013    .138614     0.05   0.960    -.2647953    .2787979
                  10  |  -.0456436   .1466993    -0.31   0.756    -.3332941    .2420069
                  11  |   -.034284   .1570102    -0.22   0.827    -.3421522    .2735842
                  12  |   .0194383   .1718525     0.11   0.910     -.317533    .3564096
                  13  |   .1623881   .2100657     0.77   0.440    -.2495122    .5742884
                  14  |   .1321682    .258112     0.51   0.609    -.3739421    .6382785
                  15  |   .5019411   .3926001     1.28   0.201    -.2678757    1.271758
                  16  |   .5209258   .2166899     2.40   0.016     .0960366     .945815
                  17  |   .0616397   .1208384     0.51   0.610    -.1753022    .2985817
                      |
           inputstate |
              Alaska  |  -.3111986   .6418187    -0.48   0.628    -1.569687    .9472902
             Arizona  |  -.0157383    .249495    -0.06   0.950    -.5049522    .4734757
            Arkansas  |     .27353   .2604426     1.05   0.294    -.2371503    .7842103
          California  |  -.0525288   .2110678    -0.25   0.803    -.4663942    .3613365
            Colorado  |   .2367953   .2556282     0.93   0.354    -.2644447    .7380354
         Connecticut  |   .4778116    .264456     1.81   0.071    -.0407382    .9963613
            Delaware  |  -.1467914   .3755695    -0.39   0.696    -.8832143    .5896315
District of Columbia  |   .2609048   .5433872     0.48   0.631     -.804578    1.326388
             Florida  |   .1507276   .2059127     0.73   0.464    -.2530294    .5544845
             Georgia  |   .1716784   .2174447     0.79   0.430    -.2546909    .5980477
              Hawaii  |   .4121683   .3403332     1.21   0.226    -.2551626    1.079499
               Idaho  |  -.4025052   .3412483    -1.18   0.238    -1.071631    .2666202
            Illinois  |   .1031968   .2272597     0.45   0.650    -.3424178    .5488113
             Indiana  |   .1022434   .2369084     0.43   0.666    -.3622906    .5667773
                Iowa  |   .4767576   .2663385     1.79   0.074    -.0454835    .9989986
              Kansas  |   .3477933   .2810064     1.24   0.216    -.2032088    .8987953
            Kentucky  |   .2710735   .2322349     1.17   0.243    -.1842965    .7264436
           Louisiana  |   .3082147   .2627788     1.17   0.241    -.2070464    .8234757
               Maine  |   .3808264   .3344231     1.14   0.255    -.2749159    1.036569
            Maryland  |  -.1805123   .3117224    -0.58   0.563    -.7917429    .4307182
       Massachusetts  |   .0908403   .2518017     0.36   0.718    -.4028968    .5845773
            Michigan  |   .0832592   .2265551     0.37   0.713    -.3609737    .5274922
           Minnesota  |  -.0401087   .2719848    -0.15   0.883     -.573421    .4932036
         Mississippi  |   .0215663    .284028     0.08   0.939    -.5353604    .5784931
            Missouri  |   .2694951   .2254445     1.20   0.232    -.1725602    .7115504
             Montana  |  -1.246371   .4074218    -3.06   0.002    -2.045251    -.447492
            Nebraska  |   -.257649   .4904668    -0.53   0.599    -1.219364    .7040665
              Nevada  |   .2214774   .2612333     0.85   0.397    -.2907533     .733708
       New Hampshire  |   .0560697   .3051561     0.18   0.854    -.5422855     .654425
          New Jersey  |     .11324   .2578339     0.44   0.661    -.3923251    .6188051
          New Mexico  |   .0979193   .2908603     0.34   0.736    -.4724045    .6682432
            New York  |   .2110917   .2085269     1.01   0.311    -.1977912    .6199747
      North Carolina  |   .1697473   .2425453     0.70   0.484    -.3058396    .6453342
        North Dakota  |  -.8600096   .3862979    -2.23   0.026    -1.617469   -.1025503
                Ohio  |    .164768    .230339     0.72   0.474    -.2868845    .6164205
            Oklahoma  |  -.0846783   .2922188    -0.29   0.772    -.6576659    .4883093
              Oregon  |  -.0019872    .261165    -0.01   0.994    -.5140839    .5101095
        Pennsylvania  |   .2360276   .2130158     1.11   0.268    -.1816574    .6537126
        Rhode Island  |   .3705128   .4078738     0.91   0.364    -.4292529    1.170279
      South Carolina  |   .2090538   .2704559     0.77   0.440    -.3212607    .7393682
        South Dakota  |   .0599981   .4277792     0.14   0.888    -.7787986    .8987947
           Tennessee  |  -.0860781   .2504836    -0.34   0.731    -.5772307    .4050744
               Texas  |   .1489841   .2103377     0.71   0.479    -.2634497    .5614178
                Utah  |   .0920883   .2934849     0.31   0.754    -.4833819    .6675585
             Vermont  |    .371199    .356678     1.04   0.298    -.3281812    1.070579
            Virginia  |   .2135739    .228019     0.94   0.349    -.2335295    .6606773
          Washington  |   -.087134   .2503339    -0.35   0.728    -.5779929    .4037249
       West Virginia  |   .2480142   .3034871     0.82   0.414    -.3470683    .8430967
           Wisconsin  |  -.0348734   .2424284    -0.14   0.886    -.5102311    .4404843
             Wyoming  |  -.1512371   1.131539    -0.13   0.894    -2.369977    2.067503
                      |
                rural |
                   2  |  -.0549134   .0615216    -0.89   0.372    -.1755459    .0657191
                   3  |   .0855498   .0830089     1.03   0.303    -.0772154    .2483151
                   4  |  -.0195898   .1192589    -0.16   0.870    -.2534346     .214255
                   5  |   .0294339   .1658684     0.18   0.859    -.2958036    .3546715
                   6  |   .1625575    .101756     1.60   0.110    -.0369674    .3620825
                   7  |   .2921942   .1394705     2.10   0.036     .0187181    .5656703
                   8  |  -.0800436   .3117514    -0.26   0.797     -.691331    .5312437
                   9  |  -.1548563   .2383103    -0.65   0.516    -.6221392    .3124266
                      |
                _cons |    3.65775   .2451257    14.92   0.000     3.177103    4.138396
---------------------------------------------------------------------------------------

. esttab sate_w1 sate_w1_c using "tables/S4.csv", b(3) se(3) wide label nobaselevels csv replac
> e
(output written to tables/S4.csv)

. 
. margins entry_exp_recode

Predictive margins                                       Number of obs = 2,871
Model VCE: Robust

Expression: Linear prediction, predict()

----------------------------------------------------------------------------------
                 |            Delta-method
                 |     Margin   std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
entry_exp_recode |
        Britain  |   3.760222   .0407314    92.32   0.000     3.680355    3.840089
          China  |   3.858782   .0411931    93.68   0.000     3.778009    3.939554
          Italy  |   4.043967   .0385107   105.01   0.000     3.968454    4.119479
----------------------------------------------------------------------------------

. preserve

. matrix preds=r(table)

. matrix b = preds[1, 1 .. 3]'

. matrix se = preds[2, 1 .. 3]'

. svmat b

. replace b1 = b1[_n-2] in 5
(1 real change made)

. replace b1 = b1[_n-1] in 3
(1 real change made)

. replace b1 = . in 2
(1 real change made, 1 to missing)

. svmat se

. replace se1 = se1[_n-2] in 5
(1 real change made)

. replace se1 = se1[_n-1] in 3
(1 real change made)

. replace se1 = . in 2
(1 real change made, 1 to missing)

. gen hi = b1+1.96*se1
(4,347 missing values generated)

. gen lo = b1-1.96*se1
(4,347 missing values generated)

. gen country = _n in 1/5
(4,345 missing values generated)

. 
. twoway (bar b1 country) (rspike hi lo country, lwidth(medium) color(black) ///
>         xlabel( 1 "Great Britain" 3 "China" 5 "Italy", noticks) xtitle("") ylabel(1(1)5) lege
> nd(off) ytitle("Support Entry Ban (1-5)"))

. graph export "figures/1.png", as(png) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/1.png saved as
    PNG format

. graph export "figures/1.pdf", as(pdf) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/1.pdf saved as
    PDF format

. 
. restore

. 
. 
. 
. * CATE estimates
. 
. eststo cates_w1: reg banentry_w1 i.entry_exp_recode##i.democrat##c.racialresentment  i.agecat
>  gender white married i.educ i.income i.inputstate i.rural [pweight=weight], robust
(sum of wgt is 2,875.03568620417)

Linear regression                               Number of obs     =      2,871
                                                F(102, 2768)      =       7.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1873
                                                Root MSE          =     1.0855

---------------------------------------------------------------------------------------------
                            |               Robust
                banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
           entry_exp_recode |
                     China  |   .4905347   .5181204     0.95   0.344    -.5254068    1.506476
                     Italy  |   .7211656   .5080928     1.42   0.156    -.2751136    1.717445
                            |
                   democrat |
                  Democrat  |   .2187443   .4460629     0.49   0.624    -.6559053    1.093394
                     Other  |  -.0662175   .4622302    -0.14   0.886    -.9725684    .8401334
                            |
  entry_exp_recode#democrat |
            China#Democrat  |  -1.142641   .5572526    -2.05   0.040    -2.235314    -.049968
               China#Other  |  -.7205831   .5749788    -1.25   0.210    -1.848014    .4068476
            Italy#Democrat  |  -.5591336   .5438301    -1.03   0.304    -1.625487    .5072202
               Italy#Other  |  -.5853674   .5698739    -1.03   0.304    -1.702788    .5320535
                            |
           racialresentment |   .2084535   .0970791     2.15   0.032     .0180986    .3988083
                            |
           entry_exp_recode#|
         c.racialresentment |
                     China  |  -.0056636   .1173918    -0.05   0.962     -.235848    .2245208
                     Italy  |  -.0711881   .1154082    -0.62   0.537    -.2974829    .1551067
                            |
democrat#c.racialresentment |
                  Democrat  |  -.0986898   .1109817    -0.89   0.374    -.3163051    .1189255
                     Other  |   .0022208   .1086436     0.02   0.984      -.21081    .2152516
                            |
  entry_exp_recode#democrat#|
         c.racialresentment |
            China#Democrat  |    .208289   .1384985     1.50   0.133    -.0632817    .4798597
               China#Other  |   .0791553   .1349546     0.59   0.558    -.1854665    .3437771
            Italy#Democrat  |   .1113405   .1353065     0.82   0.411    -.1539715    .3766524
               Italy#Other  |   .0740631   .1361423     0.54   0.586    -.1928876    .3410137
                            |
                     agecat |
                       30-  |   .0589229   .0769979     0.77   0.444    -.0920563    .2099021
                       45-  |   .2075733   .0747702     2.78   0.006     .0609623    .3541843
                       65-  |   .1572004   .0828877     1.90   0.058    -.0053276    .3197284
                            |
                     gender |   .1406469   .0448304     3.14   0.002     .0527425    .2285512
                      white |   -.032751    .056656    -0.58   0.563    -.1438432    .0783413
                    married |    .021542   .0497521     0.43   0.665    -.0760129    .1190969
                            |
                       educ |
      High school graduate  |  -.3501493   .1142164    -3.07   0.002    -.5741074   -.1261913
              Some college  |  -.4885125   .1172992    -4.16   0.000    -.7185153   -.2585097
                    2-year  |  -.3285474   .1245487    -2.64   0.008    -.5727651   -.0843298
                    4-year  |  -.6618461   .1207924    -5.48   0.000    -.8986984   -.4249938
                 Post-grad  |   -.603273   .1314341    -4.59   0.000    -.8609918   -.3455543
                            |
                     income |
                         2  |   .1128462   .1235365     0.91   0.361    -.1293868    .3550792
                         3  |   .0490756   .1160148     0.42   0.672    -.1784088      .27656
                         4  |  -.0840776   .1281143    -0.66   0.512    -.3352869    .1671317
                         5  |  -.0197398   .1230101    -0.16   0.873    -.2609406    .2214611
                         6  |    .099273   .1303711     0.76   0.446    -.1563615    .3549074
                         7  |   .2401291   .1319455     1.82   0.069    -.0185925    .4988507
                         8  |  -.0694401   .1335163    -0.52   0.603    -.3312417    .1923615
                         9  |   .0200944    .127359     0.16   0.875    -.2296337    .2698226
                        10  |  -.0238576   .1360183    -0.18   0.861    -.2905652      .24285
                        11  |  -.0418279    .149346    -0.28   0.779    -.3346688    .2510129
                        12  |   .0036917   .1639505     0.02   0.982    -.3177858    .3251693
                        13  |   .1298963   .1869606     0.69   0.487    -.2366999    .4964926
                        14  |   .0776784   .2253269     0.34   0.730    -.3641475    .5195043
                        15  |   .4318177   .1920021     2.25   0.025     .0553359    .8082994
                        16  |   .4067349   .2048415     1.99   0.047     .0050773    .8083924
                        17  |   .0257425   .1119258     0.23   0.818     -.193724     .245209
                            |
                 inputstate |
                    Alaska  |   -.444183   .6088221    -0.73   0.466    -1.637974    .7496085
                   Arizona  |  -.0209573   .2236535    -0.09   0.925    -.4595018    .4175872
                  Arkansas  |   .2840992   .2500188     1.14   0.256    -.2061431    .7743414
                California  |  -.0717928   .2013194    -0.36   0.721    -.4665442    .3229587
                  Colorado  |   .2095841   .2295216     0.91   0.361    -.2404667     .659635
               Connecticut  |   .5470586   .2442184     2.24   0.025       .06819    1.025927
                  Delaware  |  -.0050471   .3721695    -0.01   0.989     -.734805    .7247109
      District of Columbia  |   .3987933   .5177399     0.77   0.441    -.6164022    1.413989
                   Florida  |   .1272411   .1960227     0.65   0.516    -.2571244    .5116067
                   Georgia  |   .1117036   .2063878     0.54   0.588    -.2929859    .5163931
                    Hawaii  |    .664299   .3363753     1.97   0.048     .0047271    1.323871
                     Idaho  |  -.3931088    .345211    -1.14   0.255    -1.070006    .2837883
                  Illinois  |   .1328141   .2113058     0.63   0.530    -.2815189     .547147
                   Indiana  |   .1172465   .2309585     0.51   0.612    -.3356217    .5701148
                      Iowa  |   .5091605   .2596138     1.96   0.050     .0001043    1.018217
                    Kansas  |   .3136575   .2405647     1.30   0.192    -.1580469     .785362
                  Kentucky  |   .2110473   .2180061     0.97   0.333    -.2164238    .6385183
                 Louisiana  |   .2816731   .2395287     1.18   0.240    -.1879999    .7513462
                     Maine  |   .5133656   .3287763     1.56   0.119     -.131306    1.158037
                  Maryland  |  -.0963498    .297049    -0.32   0.746    -.6788098    .4861102
             Massachusetts  |    .207328   .2381533     0.87   0.384    -.2596481    .6743041
                  Michigan  |   .0622609   .2121578     0.29   0.769    -.3537427    .4782644
                 Minnesota  |  -.0770678   .2595592    -0.30   0.767     -.586017    .4318814
               Mississippi  |   -.173614   .2792204    -0.62   0.534    -.7211153    .3738873
                  Missouri  |   .3180433   .2106885     1.51   0.131    -.0950792    .7311657
                   Montana  |  -1.187952    .385477    -3.08   0.002    -1.943803   -.4321003
                  Nebraska  |  -.1899917   .4274428    -0.44   0.657    -1.028131    .6481473
                    Nevada  |   .1803086   .2458574     0.73   0.463    -.3017739    .6623911
             New Hampshire  |  -.0010798   .2880021    -0.00   0.997    -.5658004    .5636409
                New Jersey  |   .0721999   .2500404     0.29   0.773    -.4180847    .5624846
                New Mexico  |   .0416643   .2987568     0.14   0.889    -.5441443     .627473
                  New York  |   .2211349   .1981209     1.12   0.264    -.1673448    .6096145
            North Carolina  |   .1841727   .2359615     0.78   0.435    -.2785057     .646851
              North Dakota  |  -.9018286   .3791892    -2.38   0.017    -1.645351   -.1583062
                      Ohio  |   .2161513   .2203529     0.98   0.327    -.2159214    .6482239
                  Oklahoma  |   -.177676   .2757207    -0.64   0.519     -.718315     .362963
                    Oregon  |   .1054125   .2577585     0.41   0.683     -.400006    .6108309
              Pennsylvania  |   .2172911   .1994868     1.09   0.276    -.1738668     .608449
              Rhode Island  |   .4471108   .3957415     1.13   0.259    -.3288676    1.223089
            South Carolina  |      .2101   .2646893     0.79   0.427    -.3089084    .7291084
              South Dakota  |  -.1358855   .4333563    -0.31   0.754    -.9856197    .7138488
                 Tennessee  |  -.0616945     .23614    -0.26   0.794    -.5247229    .4013339
                     Texas  |   .0816815   .1979892     0.41   0.680    -.3065399     .469903
                      Utah  |   .3539449   .3511772     1.01   0.314    -.3346508    1.042541
                   Vermont  |   .4830558   .3126528     1.55   0.122    -.1300005    1.096112
                  Virginia  |   .2088268   .2117687     0.99   0.324    -.2064139    .6240675
                Washington  |  -.0106454   .2381164    -0.04   0.964     -.477549    .4562583
             West Virginia  |   .2768038   .2962213     0.93   0.350    -.3040333     .857641
                 Wisconsin  |  -.0755437   .2299602    -0.33   0.743    -.5264546    .3753672
                   Wyoming  |  -.1858771   1.255729    -0.15   0.882    -2.648137    2.276383
                            |
                      rural |
                         2  |  -.0775862   .0592955    -1.31   0.191    -.1938541    .0386817
                         3  |   .0799108   .0768457     1.04   0.298    -.0707699    .2305916
                         4  |  -.0580595   .1095893    -0.53   0.596    -.2729446    .1568255
                         5  |  -.0114088   .1615307    -0.07   0.944    -.3281417    .3053242
                         6  |   .0297741   .0987292     0.30   0.763    -.1638162    .2233643
                         7  |   .1833894    .139914     1.31   0.190     -.090957    .4577358
                         8  |  -.2813092   .3130349    -0.90   0.369    -.8951147    .3324963
                         9  |  -.1461764   .2198684    -0.66   0.506    -.5772991    .2849462
                            |
                      _cons |   3.127553   .4820346     6.49   0.000     2.182369    4.072736
---------------------------------------------------------------------------------------------

. esttab cates_w1, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                      banentry_w1                
-------------------------------------------------
China                       0.491         (0.518)
Italy                       0.721         (0.508)
Democrat                    0.219         (0.446)
Other                      -0.066         (0.462)
China # Democrat           -1.143*        (0.557)
China # Other              -0.721         (0.575)
Italy # Democrat           -0.559         (0.544)
Italy # Other              -0.585         (0.570)
racialresentment            0.208*        (0.097)
China # racialrese~t       -0.006         (0.117)
Italy # racialrese~t       -0.071         (0.115)
Democrat # racialr~t       -0.099         (0.111)
Other # racialrese~t        0.002         (0.109)
China # Democrat #~m        0.208         (0.138)
China # Other # ra~t        0.079         (0.135)
Italy # Democrat #~m        0.111         (0.135)
Italy # Other # ra~t        0.074         (0.136)
30-                         0.059         (0.077)
45-                         0.208**       (0.075)
65-                         0.157         (0.083)
gender                      0.141**       (0.045)
white                      -0.033         (0.057)
married                     0.022         (0.050)
High school graduate       -0.350**       (0.114)
Some college               -0.489***      (0.117)
2-year                     -0.329**       (0.125)
4-year                     -0.662***      (0.121)
Post-grad                  -0.603***      (0.131)
income=2                    0.113         (0.124)
income=3                    0.049         (0.116)
income=4                   -0.084         (0.128)
income=5                   -0.020         (0.123)
income=6                    0.099         (0.130)
income=7                    0.240         (0.132)
income=8                   -0.069         (0.134)
income=9                    0.020         (0.127)
income=10                  -0.024         (0.136)
income=11                  -0.042         (0.149)
income=12                   0.004         (0.164)
income=13                   0.130         (0.187)
income=14                   0.078         (0.225)
income=15                   0.432*        (0.192)
income=16                   0.407*        (0.205)
income=17                   0.026         (0.112)
Alaska                     -0.444         (0.609)
Arizona                    -0.021         (0.224)
Arkansas                    0.284         (0.250)
California                 -0.072         (0.201)
Colorado                    0.210         (0.230)
Connecticut                 0.547*        (0.244)
Delaware                   -0.005         (0.372)
District of Columbia        0.399         (0.518)
Florida                     0.127         (0.196)
Georgia                     0.112         (0.206)
Hawaii                      0.664*        (0.336)
Idaho                      -0.393         (0.345)
Illinois                    0.133         (0.211)
Indiana                     0.117         (0.231)
Iowa                        0.509*        (0.260)
Kansas                      0.314         (0.241)
Kentucky                    0.211         (0.218)
Louisiana                   0.282         (0.240)
Maine                       0.513         (0.329)
Maryland                   -0.096         (0.297)
Massachusetts               0.207         (0.238)
Michigan                    0.062         (0.212)
Minnesota                  -0.077         (0.260)
Mississippi                -0.174         (0.279)
Missouri                    0.318         (0.211)
Montana                    -1.188**       (0.385)
Nebraska                   -0.190         (0.427)
Nevada                      0.180         (0.246)
New Hampshire              -0.001         (0.288)
New Jersey                  0.072         (0.250)
New Mexico                  0.042         (0.299)
New York                    0.221         (0.198)
North Carolina              0.184         (0.236)
North Dakota               -0.902*        (0.379)
Ohio                        0.216         (0.220)
Oklahoma                   -0.178         (0.276)
Oregon                      0.105         (0.258)
Pennsylvania                0.217         (0.199)
Rhode Island                0.447         (0.396)
South Carolina              0.210         (0.265)
South Dakota               -0.136         (0.433)
Tennessee                  -0.062         (0.236)
Texas                       0.082         (0.198)
Utah                        0.354         (0.351)
Vermont                     0.483         (0.313)
Virginia                    0.209         (0.212)
Washington                 -0.011         (0.238)
West Virginia               0.277         (0.296)
Wisconsin                  -0.076         (0.230)
Wyoming                    -0.186         (1.256)
Rural Zip Code Ind~2       -0.078         (0.059)
Rural Zip Code Ind~3        0.080         (0.077)
Rural Zip Code Ind~4       -0.058         (0.110)
Rural Zip Code Ind~5       -0.011         (0.162)
Rural Zip Code Ind~6        0.030         (0.099)
Rural Zip Code Ind~7        0.183         (0.140)
Rural Zip Code Ind~8       -0.281         (0.313)
Rural Zip Code Ind~9       -0.146         (0.220)
Constant                    3.128***      (0.482)
-------------------------------------------------
Observations                 2871                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_w1 using "tables/S5.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S5.csv)

. 
. 
. tab entry_exp_recode democrat if racialresentment <= `race_median'

entry_exp_ |             democrat
    recode | Republica   Democrat      Other |     Total
-----------+---------------------------------+----------
   Britain |        54        307        202 |       563 
     China |        67        288        175 |       530 
     Italy |        52        315        191 |       558 
-----------+---------------------------------+----------
     Total |       173        910        568 |     1,651 

. margins entry_exp_recode if racialresentment<=`race_median', at(democrat=(0 1 2))

Predictive margins                                       Number of obs = 1,573
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: democrat = 0
2._at: democrat = 1
3._at: democrat = 2

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |     Margin   std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
_at#entry_exp_recode |
          1#Britain  |   3.548503   .2310979    15.35   0.000     3.095361    4.001645
            1#China  |   4.027449   .1685529    23.89   0.000     3.696947    4.357951
            1#Italy  |   4.124004   .1596484    25.83   0.000     3.810962    4.437046
          2#Britain  |   3.565309   .0675871    52.75   0.000     3.432783    3.697836
            2#China  |   3.327813    .078746    42.26   0.000     3.173406     3.48222
            2#Italy  |   3.809501    .067881    56.12   0.000     3.676398    3.942603
          3#Britain  |    3.48683   .0965476    36.12   0.000     3.297517    3.676142
            3#China  |   3.407159   .0899768    37.87   0.000     3.230731    3.583588
            3#Italy  |   3.628511   .0876251    41.41   0.000     3.456694    3.800328
--------------------------------------------------------------------------------------

. preserve

. matrix preds=r(table)

. matrix b = preds[1, 1 .. 9]'

. matrix se = preds[2, 1 .. 9]'

. g at_race = 1 if mod(_n,3)==1
(2,900 missing values generated)

. replace at_race = 2 if mod(_n,3)==2
(1,450 real changes made)

. replace at_race = 3 if mod(_n,3)==0
(1,450 real changes made)

. label define at_race 1 "British" 2 "Chinese" 3 "Italian", replace

. label values at_race at_race

. g at_dem = 0 in 1/3
(4,347 missing values generated)

. replace at_dem = 1 in 4/6
(3 real changes made)

. replace at_dem = 2 in 7/9
(3 real changes made)

. svmat b

. svmat se

. gen hi = b1+1.96*se1
(4,341 missing values generated)

. gen lo = b1-1.96*se1
(4,341 missing values generated)

. gen demrace = at_race if at_dem == 0
(4,347 missing values generated)

. replace demrace = at_race + 4 if at_dem == 1
(3 real changes made)

. replace demrace = at_race + 8 if at_dem == 2
(3 real changes made)

. twoway (bar b1 demrace if at_race==1, ylabel(3(1)5) color(red))  (bar b1 demrace if at_race==
> 2, color(blue)) (bar b1 demrace if at_race==3, color(gs12)) ///
>         (rspike hi lo demrace, lwidth(medium) color(black)), ///
>         xlabel( 2 "R" 6 "D" 10 "O", noticks) xtitle("") legend(row(1) order(1 "Great Britain"
>  2 "China" 3 "Italy") )  ///
>         ytitle("Support Entry Ban (1-5)") yline(`mean_b', lpattern(dash)) name(imm_hi, replac
> e) title("Low Racial Resentment Score")

. restore

. 
. tab entry_exp_recode democrat if racialresentment > `race_median'

entry_exp_ |             democrat
    recode | Republica   Democrat      Other |     Total
-----------+---------------------------------+----------
   Britain |       187         86        165 |       438 
     China |       207         90        149 |       446 
     Italy |       208         90        167 |       465 
-----------+---------------------------------+----------
     Total |       602        266        481 |     1,349 

. margins entry_exp_recode if racialresentment > `race_median', at(democrat=(0 1 2))

Predictive margins                                       Number of obs = 1,298
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: democrat = 0
2._at: democrat = 1
3._at: democrat = 2

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |     Margin   std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
_at#entry_exp_recode |
          1#Britain  |   4.098996   .0780925    52.49   0.000     3.945871    4.252122
            1#China  |    4.56362   .0560679    81.39   0.000     4.453681     4.67356
            1#Italy  |    4.49448   .0661459    67.95   0.000      4.36478     4.62418
          2#Britain  |    3.86624   .1396203    27.69   0.000     3.592469     4.14001
            2#China  |   4.141135   .1180016    35.09   0.000     3.909755    4.372515
            2#Italy  |   4.211967   .1066888    39.48   0.000     4.002769    4.421165
          3#Britain  |   4.042939    .079225    51.03   0.000     3.887593    4.198285
            3#China  |   4.149111   .0840718    49.35   0.000     3.984261    4.313961
            3#Italy  |    4.19189   .0961176    43.61   0.000      4.00342    4.380359
--------------------------------------------------------------------------------------

. preserve

. matrix preds=r(table)

. matrix b = preds[1, 1 .. 9]'

. matrix se = preds[2, 1 .. 9]'

. g at_race = 1 if mod(_n,3)==1
(2,900 missing values generated)

. replace at_race = 2 if mod(_n,3)==2
(1,450 real changes made)

. replace at_race = 3 if mod(_n,3)==0
(1,450 real changes made)

. label define at_race 1 "British" 2 "Chinese" 3 "Italian", replace

. label values at_race at_race

. g at_dem = 0 in 1/3
(4,347 missing values generated)

. replace at_dem = 1 in 4/6
(3 real changes made)

. replace at_dem = 2 in 7/9
(3 real changes made)

. svmat b

. svmat se

. gen hi = b1+1.96*se1
(4,341 missing values generated)

. gen lo = b1-1.96*se1
(4,341 missing values generated)

. gen demrace = at_race if at_dem == 0
(4,347 missing values generated)

. replace demrace = at_race + 4 if at_dem == 1
(3 real changes made)

. replace demrace = at_race + 8 if at_dem == 2
(3 real changes made)

. twoway (bar b1 demrace if at_race==1, ylabel(3(1)5) color(red))  (bar b1 demrace if at_race==
> 2, color(blue)) (bar b1 demrace if at_race==3, color(gs12)) ///
>         (rspike hi lo demrace, lwidth(medium) color(black)), ///
>         xlabel( 2 "R" 6 "D" 10 "O", noticks) xtitle("") legend(row(1) order(1 "Great Britain"
>  2 "China" 3 "Italy") )  ///
>         ytitle("Support Entry Ban (1-5)") yline(`mean_b', lpattern(dash)) name(imm_lo, replac
> e)  title("High Racial Resentment Score")

. restore

. 
. 
. margins entry_exp_recode, at(democrat=(0 1 2))

Predictive margins                                       Number of obs = 2,871
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: democrat = 0
2._at: democrat = 1
3._at: democrat = 2

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |     Margin   std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
_at#entry_exp_recode |
          1#Britain  |   3.802194   .1291267    29.45   0.000        3.549    4.055388
            1#China  |    4.27454   .0984889    43.40   0.000     4.081421    4.467659
            1#Italy  |   4.294736   .0979405    43.85   0.000     4.102692    4.486779
          2#Britain  |   3.703991   .0822363    45.04   0.000      3.54274    3.865242
            2#China  |   3.702627    .073033    50.70   0.000     3.559422    3.845832
            2#Italy  |   3.994975   .0657211    60.79   0.000     3.866107    4.123842
          3#Britain  |   3.743109   .0635354    58.91   0.000     3.618527     3.86769
            3#China  |   3.749083    .064723    57.93   0.000     3.622173    3.875993
            3#Italy  |    3.88814   .0680088    57.17   0.000     3.754787    4.021493
--------------------------------------------------------------------------------------

. preserve

. matrix preds=r(table)

. matrix b = preds[1, 1 .. 9]'

. matrix se = preds[2, 1 .. 9]'

. g at_race = 1 if mod(_n,3)==1
(2,900 missing values generated)

. replace at_race = 2 if mod(_n,3)==2
(1,450 real changes made)

. replace at_race = 3 if mod(_n,3)==0
(1,450 real changes made)

. label define at_race 1 "British" 2 "Chinese" 3 "Italian", replace

. label values at_race at_race

. g at_dem = 0 in 1/3
(4,347 missing values generated)

. replace at_dem = 1 in 4/6
(3 real changes made)

. replace at_dem = 2 in 7/9
(3 real changes made)

. svmat b

. svmat se

. gen hi = b1+1.96*se1
(4,341 missing values generated)

. gen lo = b1-1.96*se1
(4,341 missing values generated)

. gen demrace = at_race if at_dem == 0
(4,347 missing values generated)

. replace demrace = at_race + 4 if at_dem == 1
(3 real changes made)

. replace demrace = at_race + 8 if at_dem == 2
(3 real changes made)

. twoway (bar b1 demrace if at_race==1, ylabel(3(1)5) color(red))  (bar b1 demrace if at_race==
> 2, color(blue)) (bar b1 demrace if at_race==3, color(gs12)) ///
>         (rspike hi lo demrace, lwidth(medium) color(black)), ///
>         xlabel( 2 "R" 6 "D" 10 "O", noticks) xtitle("") legend(row(1) order(1 "Great Britain"
>  2 "China" 3 "Italy") )  ///
>         ytitle("Support Entry Ban (1-5)") yline(`mean_b', lpattern(dash)) name(race_all, repl
> ace)  title("All Respondents")

. restore

. 
. grc1leg imm_hi imm_lo, name(hilo, replace) 

. grc1leg hilo race_all, rows(2) note("Note: The dashed horizontal line is the mean response ac
> ross all respondents in the sample.")

. graph export "figures/2.png", as(png) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/2.png saved as
    PNG format

. graph export "figures/2.pdf", as(pdf) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/2.pdf saved as
    PDF format

. 
. 
. 
. 
. * FOUR WAVES 
. 
. preserve

. 
. import excel "country_data.xlsx", sheet("Sheet1") firstrow clear
(61 vars, 12 obs)

. gen banentryexp_w=1 if location=="China"
(8 missing values generated)

. replace banentryexp_w=2 if location=="Brazil"
(4 real changes made)

. replace banentryexp_w=3 if location=="United Kingdom"
(4 real changes made)

. gen  treatwaveid = wave*10+banentryexp_w

. 
. drop wave date

. sort treatwaveid 

. 
. save country_data.dta, replace
(file country_data.dta not found)
file country_data.dta saved

. 
. restore

. 
. 
. preserve 

. 
. keep banentryexp_w* banentry_w* democrat_w* caseid weight* aia racialresentment agecat gender
>  white married educ income inputstate rural

. drop banentryexp_w1 banentryexp_w2 banentry_w1 banentry_w2

. drop weight

. 
. reshape long banentryexp_w banentry_w democrat_w weight_w, i(caseid) j(wave)
(j = 1 2 3 4 5 6)
(variable banentryexp_w1 not found)
(variable banentry_w1 not found)
(variable banentryexp_w2 not found)
(variable banentry_w2 not found)
weight_w2:  2401 values would be changed; not changed
weight_w3:  2104 values would be changed; not changed
weight_w4:  1949 values would be changed; not changed
weight_w5:  1871 values would be changed; not changed
weight_w6:  3000 values would be changed; not changed

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            4,350   ->   26,100      
Number of variables                  31   ->   16          
j variable (6 values)                     ->   wave
xij variables:
banentryexp_w1 banentryexp_w2 ... banentryexp_w6->banentryexp_w
banentry_w1 banentry_w2 ... banentry_w6   ->   banentry_w
democrat_w1 democrat_w2 ... democrat_w6   ->   democrat_w
      weight_w1 weight_w2 ... weight_w6   ->   weight_w
-----------------------------------------------------------------------------

. label values democrat democrat

. 
. xtset caseid wave

Panel variable: caseid (strongly balanced)
 Time variable: wave, 1 to 6
         Delta: 1 unit

. gen  treatwaveid = wave*10+banentryexp_w
(17,176 missing values generated)

. sort treatwaveid

. merge m:1 treatwaveid using country_data.dta
(variable banentryexp_w was long, now double to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                        17,176
        from master                    17,176  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             8,924  (_merge==3)
    -----------------------------------------

. drop if wave==.
(0 observations deleted)

. drop _merge

. 
. 
. graph drop _all

. 
. 
. 
. * SATE Analysis
. eststo sate_w2to5: xtreg banentry_w ib3.banentryexp_w, re robust

Random-effects GLS regression                   Number of obs     =      8,924
Group variable: caseid                          Number of groups  =      3,830

R-squared:                                      Obs per group:
     Within  = 0.0348                                         min =          1
     Between = 0.0040                                         avg =        2.3
     Overall = 0.0152                                         max =          4

                                                Wald chi2(2)      =     186.44
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                              (Std. err. adjusted for 3,830 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
   banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
banentryexp_w |
       China  |   .2043799   .0321103     6.36   0.000     .1414449    .2673149
      Brazil  |   .3865144   .0283087    13.65   0.000     .3310305    .4419984
              |
        _cons |   3.020126   .0222667   135.63   0.000     2.976484    3.063768
--------------+----------------------------------------------------------------
      sigma_u |  .79364847
      sigma_e |  .93769687
          rho |  .41737171   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. eststo sate_w2to5_f: xtreg banentry_w ib3.banentryexp_w, fe robust

Fixed-effects (within) regression               Number of obs     =      8,924
Group variable: caseid                          Number of groups  =      3,830

R-squared:                                      Obs per group:
     Within  = 0.0353                                         min =          1
     Between = 0.0030                                         avg =        2.3
     Overall = 0.0147                                         max =          4

                                                F(2,3829)         =      82.24
corr(u_i, Xb) = -0.0275                         Prob > F          =     0.0000

                              (Std. err. adjusted for 3,830 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
   banentry_w | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
banentryexp_w |
       China  |   .2749602   .0370998     7.41   0.000      .202223    .3476975
      Brazil  |   .4296792   .0336559    12.77   0.000     .3636939    .4956644
              |
        _cons |   3.013396   .0203132   148.35   0.000      2.97357    3.053221
--------------+----------------------------------------------------------------
      sigma_u |  1.0866411
      sigma_e |  .93769687
          rho |  .57318062   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. eststo sate_w2to5_c: xtreg banentry_w ib3.banentryexp_w i.agecat gender white married i.educ 
> i.income i.inputstate i.rural, re robust

Random-effects GLS regression                   Number of obs     =      7,293
Group variable: caseid                          Number of groups  =      2,383

R-squared:                                      Obs per group:
     Within  = 0.0377                                         min =          1
     Between = 0.1330                                         avg =        3.1
     Overall = 0.1034                                         max =          4

                                                Wald chi2(87)     =     602.05
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                      (Std. err. adjusted for 2,383 clusters in caseid)
---------------------------------------------------------------------------------------
                      |               Robust
           banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        banentryexp_w |
               China  |   .2878411   .0353409     8.14   0.000     .2185743     .357108
              Brazil  |   .4346258   .0311722    13.94   0.000     .3735293    .4957222
                      |
               agecat |
                 30-  |   .1747803   .0694785     2.52   0.012      .038605    .3109557
                 45-  |   .4989683   .0654548     7.62   0.000     .3706793    .6272574
                 65-  |   .4663432   .0698722     6.67   0.000     .3293963    .6032902
                      |
               gender |  -.0782989   .0380234    -2.06   0.039    -.1528234   -.0037744
                white |  -.0404653   .0461461    -0.88   0.381      -.13091    .0499793
              married |   .0933885   .0412255     2.27   0.023     .0125879    .1741891
                      |
                 educ |
High school graduate  |  -.3075881    .102989    -2.99   0.003    -.5094428   -.1057333
        Some college  |  -.4748333   .1064554    -4.46   0.000     -.683482   -.2661845
              2-year  |  -.4279299   .1125702    -3.80   0.000    -.6485635   -.2072963
              4-year  |  -.6324025   .1088019    -5.81   0.000    -.8456503   -.4191547
           Post-grad  |  -.8150765   .1153382    -7.07   0.000    -1.041135   -.5890179
                      |
               income |
                   2  |   .0957258   .1112759     0.86   0.390    -.1223711    .3138226
                   3  |  -.0591293   .1084058    -0.55   0.585    -.2716007    .1533421
                   4  |  -.0868744   .1112626    -0.78   0.435     -.304945    .1311962
                   5  |  -.0869638   .1122168    -0.77   0.438    -.3069046     .132977
                   6  |  -.0608742   .1131239    -0.54   0.590    -.2825929    .1608445
                   7  |  -.1081411   .1216811    -0.89   0.374    -.3466316    .1303494
                   8  |  -.0863043   .1150233    -0.75   0.453    -.3117457    .1391371
                   9  |  -.2258036   .1151267    -1.96   0.050    -.4514478   -.0001594
                  10  |   -.154245   .1180055    -1.31   0.191    -.3855315    .0770415
                  11  |  -.2777334   .1287686    -2.16   0.031    -.5301153   -.0253515
                  12  |  -.1979611   .1357741    -1.46   0.145    -.4640734    .0681512
                  13  |  -.1670431   .1564127    -1.07   0.286    -.4736064    .1395201
                  14  |  -.1388716   .1858104    -0.75   0.455    -.5030534    .2253102
                  15  |  -1.341772   .7565321    -1.77   0.076    -2.824548    .1410033
                  16  |    .355407   .2761996     1.29   0.198    -.1859343    .8967483
                  17  |  -.0770607   .1040785    -0.74   0.459    -.2810508    .1269294
                      |
           inputstate |
              Alaska  |  -.7075561   .3624872    -1.95   0.051    -1.418018    .0029058
             Arizona  |  -.1543556   .2021297    -0.76   0.445    -.5505226    .2418114
            Arkansas  |  -.1868267   .2209943    -0.85   0.398    -.6199677    .2463142
          California  |   .0233333   .1716144     0.14   0.892    -.3130247    .3596913
            Colorado  |   .1287684   .2230835     0.58   0.564    -.3084672    .5660041
         Connecticut  |   -.120489   .2867538    -0.42   0.674    -.6825162    .4415382
            Delaware  |   .0203499   .3618676     0.06   0.955    -.6888975    .7295973
District of Columbia  |  -.3696731   .3165724    -1.17   0.243    -.9901436    .2507973
             Florida  |   .1431144   .1724319     0.83   0.407    -.1948459    .4810746
             Georgia  |  -.0106655   .1826487    -0.06   0.953    -.3686504    .3473194
              Hawaii  |   -.135153   .2880425    -0.47   0.639     -.699706    .4293999
               Idaho  |   .3825415   .2281755     1.68   0.094    -.0646743    .8297573
            Illinois  |  -.0462871   .1891985    -0.24   0.807    -.4171093    .3245351
             Indiana  |  -.0419275   .2102901    -0.20   0.842    -.4540886    .3702336
                Iowa  |   .3460638   .2178843     1.59   0.112    -.0809815    .7731091
              Kansas  |   .2111318   .2716488     0.78   0.437    -.3212902    .7435537
            Kentucky  |  -.0051634   .2172272    -0.02   0.981    -.4309209     .420594
           Louisiana  |   .1865189   .2374167     0.79   0.432    -.2788093    .6518471
               Maine  |   .0578172   .2855908     0.20   0.840    -.5019305    .6175649
            Maryland  |  -.1487953   .2434994    -0.61   0.541    -.6260452    .3284547
       Massachusetts  |  -.1998392   .2095846    -0.95   0.340    -.6106174     .210939
            Michigan  |  -.0021362   .1797279    -0.01   0.991    -.3543965     .350124
           Minnesota  |  -.2846255   .2122372    -1.34   0.180    -.7006028    .1313518
         Mississippi  |   .1624689   .2246648     0.72   0.470     -.277866    .6028038
            Missouri  |  -.0538688   .2088386    -0.26   0.796    -.4631848    .3554473
             Montana  |  -.3462457   .3460899    -1.00   0.317    -1.024569     .332078
            Nebraska  |  -.1010731   .2329494    -0.43   0.664    -.5576455    .3554993
              Nevada  |  -.1627809   .2445437    -0.67   0.506    -.6420778    .3165159
       New Hampshire  |   .1851602   .2437333     0.76   0.447    -.2925482    .6628686
          New Jersey  |   .1516687   .1953226     0.78   0.437    -.2311566     .534494
          New Mexico  |  -.0913824   .2459067    -0.37   0.710    -.5733507    .3905859
            New York  |   .1895693   .1741514     1.09   0.276    -.1517612    .5308997
      North Carolina  |  -.0413601   .1914364    -0.22   0.829    -.4165684    .3338483
        North Dakota  |  -.6136059   .2956936    -2.08   0.038    -1.193155    -.034057
                Ohio  |  -.0918554    .183543    -0.50   0.617    -.4515931    .2678823
            Oklahoma  |   .2067317   .2340134     0.88   0.377    -.2519262    .6653896
              Oregon  |  -.1553209   .2201787    -0.71   0.481    -.5868633    .2762214
        Pennsylvania  |   .1379057   .1725578     0.80   0.424    -.2003014    .4761128
        Rhode Island  |  -.0994046   .4173661    -0.24   0.812    -.9174271    .7186179
      South Carolina  |  -.0743198   .2093669    -0.35   0.723    -.4846714    .3360318
        South Dakota  |  -.1692538   .3513227    -0.48   0.630    -.8578337    .5193261
           Tennessee  |   .3026499   .1994572     1.52   0.129    -.0882789    .6935787
               Texas  |   .1957827   .1753069     1.12   0.264    -.1478124    .5393778
                Utah  |   .0512788   .2280011     0.22   0.822    -.3955952    .4981528
             Vermont  |  -.2096837   .3023818    -0.69   0.488    -.8023411    .3829736
            Virginia  |   .0432599   .1958779     0.22   0.825    -.3406536    .4271735
          Washington  |  -.2765642    .192114    -1.44   0.150    -.6531007    .0999722
       West Virginia  |   .0488209     .24807     0.20   0.844    -.4373874    .5350292
           Wisconsin  |  -.0050983   .1934789    -0.03   0.979    -.3843099    .3741133
             Wyoming  |   .6022687   .4687384     1.28   0.199    -.3164416    1.520979
                      |
                rural |
                   2  |  -.0331325   .0532508    -0.62   0.534    -.1375021    .0712372
                   3  |   .1127537   .0623362     1.81   0.070     -.009423    .2349304
                   4  |   .1045862   .0827307     1.26   0.206    -.0575631    .2667355
                   5  |   .1666575   .1150992     1.45   0.148    -.0589328    .3922477
                   6  |    .110037   .0910301     1.21   0.227    -.0683787    .2884527
                   7  |   .1105769   .1067958     1.04   0.300     -.098739    .3198928
                   8  |   .0931095   .2268511     0.41   0.681    -.3515105    .5377294
                   9  |   .1711711   .2719706     0.63   0.529    -.3618815    .7042237
                      |
                _cons |   3.317939   .2090798    15.87   0.000      2.90815    3.727728
----------------------+----------------------------------------------------------------
              sigma_u |  .72809629
              sigma_e |  .93758395
                  rho |   .3761913   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

. esttab sate_w2to5 sate_w2to5_c using "tables/S6.csv", b(3) se(3) wide label nobaselevels csv 
> replace
(output written to tables/S6.csv)

. 
. * CATE estimates by wave
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs              18,000
25%            2              1       Sum of wgt.      18,000

50%            3                      Mean              3.181
                        Largest       Std. dev.      1.459803
75%            5              5
90%            5              5       Variance       2.131024
95%            5              5       Skewness      -.2031789
99%            5              5       Kurtosis        1.70661

. local race_median = r(p50)

. 
. replace wave = wave-1
(26,100 real changes made)

. 
. eststo cates_w2to5: xtreg banentry_w i.banentryexp_w##i.wave##i.democrat_w##c.racialresentmen
> t  i.agecat gender white married i.educ i.income i.inputstate i.rural, re robust

Random-effects GLS regression                   Number of obs     =      6,997
Group variable: caseid                          Number of groups  =      2,300

R-squared:                                      Obs per group:
     Within  = 0.1761                                         min =          1
     Between = 0.3037                                         avg =        3.0
     Overall = 0.2585                                         max =          4

                                                Wald chi2(156)    =    2470.50
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                          (Std. err. adjusted for 2,300 clusters in caseid)
-------------------------------------------------------------------------------------------
                          |               Robust
               banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
            banentryexp_w |
                  Brazil  |   .0845062   .5158013     0.16   0.870    -.9264457    1.095458
           Great Britain  |   -.419891   .5523531    -0.76   0.447    -1.502483    .6627012
                          |
                     wave |
                       3  |   .2020111   .4723779     0.43   0.669    -.7238327    1.127855
                       4  |  -.5640067   .4987569    -1.13   0.258    -1.541552    .4135389
                       5  |  -.9091342   .8045106    -1.13   0.258    -2.485946    .6676776
                          |
       banentryexp_w#wave |
                Brazil#3  |  -.2115131   .6785074    -0.31   0.755    -1.541363    1.118337
                Brazil#4  |   .4394699   .6400726     0.69   0.492    -.8150493    1.693989
                Brazil#5  |   .1348279   .9560008     0.14   0.888    -1.738899    2.008555
         Great Britain#3  |  -.5955472   .6072555    -0.98   0.327    -1.785746    .5946517
         Great Britain#4  |   .3779697   .6926765     0.55   0.585    -.9796513    1.735591
         Great Britain#5  |   .8193406   1.040771     0.79   0.431    -1.220532    2.859214
                          |
               democrat_w |
                Democrat  |  -1.124461   .4788349    -2.35   0.019     -2.06296   -.1859619
                   Other  |  -1.569839    .487428    -3.22   0.001    -2.525181    -.614498
                          |
 banentryexp_w#democrat_w |
         Brazil#Democrat  |   .8033303   .5563536     1.44   0.149    -.2871026    1.893763
            Brazil#Other  |   1.187276   .5745615     2.07   0.039     .0611566    2.313396
  Great Britain#Democrat  |   .9347614   .5863345     1.59   0.111    -.2144332    2.083956
     Great Britain#Other  |   1.702926   .5949359     2.86   0.004     .5368734    2.868979
                          |
          wave#democrat_w |
              3#Democrat  |   .0496146   .5066075     0.10   0.922    -.9433179    1.042547
                 3#Other  |   .3912218   .5227125     0.75   0.454    -.6332759    1.415719
              4#Democrat  |   .4129367   .5403137     0.76   0.445    -.6460587    1.471932
                 4#Other  |   .4682252   .5629426     0.83   0.406     -.635122    1.571572
              5#Democrat  |   .4520687   .8361107     0.54   0.589    -1.186678    2.090815
                 5#Other  |   .2107827   .8460089     0.25   0.803    -1.447364     1.86893
                          |
       banentryexp_w#wave#|
               democrat_w |
       Brazil#3#Democrat  |   .3999891   .7312416     0.55   0.584    -1.033218    1.833196
          Brazil#3#Other  |  -.0117417   .7604605    -0.02   0.988    -1.502217    1.478734
       Brazil#4#Democrat  |  -.8784394   .7039406    -1.25   0.212    -2.258138    .5012589
          Brazil#4#Other  |  -.6311251   .7418453    -0.85   0.395    -2.085115     .822865
       Brazil#5#Democrat  |   .4924471   1.005867     0.49   0.624    -1.479017    2.463911
          Brazil#5#Other  |   .1783826   1.031933     0.17   0.863    -1.844169    2.200934
Great Britain#3#Democrat  |   .3519982   .6642334     0.53   0.596    -.9498753    1.653872
   Great Britain#3#Other  |  -.0040984   .6841127    -0.01   0.995    -1.344935    1.336738
Great Britain#4#Democrat  |   -.428924   .7494523    -0.57   0.567    -1.897823    1.039976
   Great Britain#4#Other  |  -.5931643   .7744497    -0.77   0.444    -2.111058    .9247292
Great Britain#5#Democrat  |  -.3230197   1.086308    -0.30   0.766    -2.452144    1.806105
   Great Britain#5#Other  |   .1709266   1.114574     0.15   0.878    -2.013599    2.355452
                          |
         racialresentment |   .2313439   .1011204     2.29   0.022     .0331515    .4295363
                          |
            banentryexp_w#|
       c.racialresentment |
                  Brazil  |  -.0700774   .1146362    -0.61   0.541    -.2947602    .1546053
           Great Britain  |  -.1066378   .1238491    -0.86   0.389    -.3493775     .136102
                          |
  wave#c.racialresentment |
                       3  |  -.0263199   .1050007    -0.25   0.802    -.2321175    .1794776
                       4  |   .0384448   .1103436     0.35   0.728    -.1778247    .2547142
                       5  |   .0657558   .1805913     0.36   0.716    -.2881966    .4197082
                          |
       banentryexp_w#wave#|
       c.racialresentment |
                Brazil#3  |   .0169276   .1499727     0.11   0.910    -.2770135    .3108686
                Brazil#4  |  -.1399449   .1447886    -0.97   0.334    -.4237253    .1438356
                Brazil#5  |  -.0132129   .2126905    -0.06   0.950    -.4300787    .4036528
         Great Britain#3  |    .104709   .1389718     0.75   0.451    -.1676708    .3770888
         Great Britain#4  |  -.0945954   .1557627    -0.61   0.544    -.3998847     .210694
         Great Britain#5  |  -.1326431   .2350042    -0.56   0.572    -.5932429    .3279567
                          |
               democrat_w#|
       c.racialresentment |
                Democrat  |   .1923783   .1122902     1.71   0.087    -.0277065     .412463
                   Other  |   .2725334   .1108099     2.46   0.014       .05535    .4897167
                          |
 banentryexp_w#democrat_w#|
       c.racialresentment |
         Brazil#Democrat  |  -.1816687   .1358512    -1.34   0.181     -.447932    .0845947
            Brazil#Other  |  -.2647399   .1325681    -2.00   0.046    -.5245686   -.0049112
  Great Britain#Democrat  |  -.1650684   .1427569    -1.16   0.248    -.4448669    .1147301
     Great Britain#Other  |  -.3567817   .1379113    -2.59   0.010    -.6270829   -.0864805
                          |
          wave#democrat_w#|
       c.racialresentment |
              3#Democrat  |  -.0486173   .1230994    -0.39   0.693    -.2898878    .1926532
                 3#Other  |  -.0786565   .1199239    -0.66   0.512    -.3137029      .15639
              4#Democrat  |  -.2109869   .1324072    -1.59   0.111    -.4705003    .0485264
                 4#Other  |  -.1067804   .1323006    -0.81   0.420    -.3660848    .1525241
              5#Democrat  |  -.1789968   .1976581    -0.91   0.365    -.5663996     .208406
                 5#Other  |  -.0524733   .1941248    -0.27   0.787    -.4329509    .3280042
                          |
       banentryexp_w#wave#|
               democrat_w#|
       c.racialresentment |
       Brazil#3#Democrat  |   -.047552   .1768295    -0.27   0.788    -.3941314    .2990274
          Brazil#3#Other  |   .0127843   .1755685     0.07   0.942    -.3313235    .3568922
       Brazil#4#Democrat  |   .3500578   .1797069     1.95   0.051    -.0021612    .7022768
          Brazil#4#Other  |   .1734762   .1776814     0.98   0.329    -.1747728    .5217253
       Brazil#5#Democrat  |    .001932   .2425322     0.01   0.994    -.4734224    .4772863
          Brazil#5#Other  |   .0169031   .2368715     0.07   0.943    -.4473565    .4811627
Great Britain#3#Democrat  |  -.0033077   .1702046    -0.02   0.984    -.3369026    .3302872
   Great Britain#3#Other  |   .0485065   .1623992     0.30   0.765    -.2697901    .3668031
Great Britain#4#Democrat  |   .2161545   .1876613     1.15   0.249    -.1516548    .5839639
   Great Britain#4#Other  |   .1737381   .1838637     0.94   0.345    -.1866281    .5341043
Great Britain#5#Democrat  |     .21031    .260557     0.81   0.420    -.3003723    .7209923
   Great Britain#5#Other  |  -.0484814    .259666    -0.19   0.852    -.5574174    .4604545
                          |
                   agecat |
                     30-  |    .146257    .065849     2.22   0.026     .0171954    .2753187
                     45-  |   .3964988   .0627452     6.32   0.000     .2735206    .5194771
                     65-  |   .3654997   .0666982     5.48   0.000     .2347737    .4962257
                          |
                   gender |  -.0634517   .0354248    -1.79   0.073     -.132883    .0059796
                    white |   -.158611   .0445178    -3.56   0.000    -.2458644   -.0713577
                  married |    .033247    .038031     0.87   0.382    -.0412925    .1077865
                          |
                     educ |
    High school graduate  |  -.2695302    .102671    -2.63   0.009    -.4707617   -.0682986
            Some college  |  -.3519086   .1057037    -3.33   0.001     -.559084   -.1447332
                  2-year  |  -.3082992   .1103797    -2.79   0.005    -.5246394   -.0919591
                  4-year  |   -.475431   .1075972    -4.42   0.000    -.6863177   -.2645443
               Post-grad  |   -.547209   .1139572    -4.80   0.000    -.7705609   -.3238571
                          |
                   income |
                       2  |   .0938482   .1024804     0.92   0.360    -.1070097    .2947061
                       3  |  -.0355174   .1019019    -0.35   0.727    -.2352414    .1642066
                       4  |  -.0800497   .1004969    -0.80   0.426      -.27702    .1169206
                       5  |  -.1355447   .1014837    -1.34   0.182    -.3344491    .0633596
                       6  |  -.0617908   .1030601    -0.60   0.549    -.2637849    .1402033
                       7  |  -.0645314   .1131145    -0.57   0.568    -.2862317    .1571689
                       8  |  -.1024993    .105575    -0.97   0.332    -.3094225     .104424
                       9  |  -.2038758   .1068098    -1.91   0.056    -.4132193    .0054676
                      10  |  -.1347048   .1084978    -1.24   0.214    -.3473567    .0779471
                      11  |  -.2085145   .1176303    -1.77   0.076    -.4390656    .0220367
                      12  |  -.2048809   .1293032    -1.58   0.113    -.4583105    .0485486
                      13  |  -.1815994   .1427173    -1.27   0.203    -.4613201    .0981213
                      14  |  -.1851026   .1725112    -1.07   0.283    -.5232184    .1530133
                      15  |  -.9879355   .3575179    -2.76   0.006    -1.688658   -.2872134
                      16  |   .1965514   .2208022     0.89   0.373     -.236213    .6293158
                      17  |  -.1351874   .0968892    -1.40   0.163    -.3250868     .054712
                          |
               inputstate |
                  Alaska  |  -.7606719   .3167065    -2.40   0.016    -1.381405   -.1399385
                 Arizona  |  -.1212049    .205955    -0.59   0.556    -.5248693    .2824595
                Arkansas  |  -.0544614   .2201609    -0.25   0.805    -.4859689     .377046
              California  |   .0043187   .1793959     0.02   0.981    -.3472908    .3559282
                Colorado  |   .2055038   .2178842     0.94   0.346    -.2215413     .632549
             Connecticut  |   .0041158   .2615561     0.02   0.987    -.5085248    .5167564
                Delaware  |   .0877151   .3374706     0.26   0.795     -.573715    .7491453
    District of Columbia  |  -.1931099   .2941912    -0.66   0.512    -.7697141    .3834943
                 Florida  |   .1296241   .1806899     0.72   0.473    -.2245216    .4837697
                 Georgia  |  -.0330199   .1870105    -0.18   0.860    -.3995537     .333514
                  Hawaii  |  -.0128818   .3027339    -0.04   0.966    -.6062293    .5804658
                   Idaho  |   .3857351   .2278285     1.69   0.090    -.0608006    .8322708
                Illinois  |   .0159243   .1961437     0.08   0.935    -.3685103     .400359
                 Indiana  |  -.0154352   .2094733    -0.07   0.941    -.4259953    .3951248
                    Iowa  |   .4334683   .2463356     1.76   0.078    -.0493407    .9162773
                  Kansas  |    .139045   .2434254     0.57   0.568    -.3380601      .61615
                Kentucky  |  -.0297635   .2152307    -0.14   0.890    -.4516079     .392081
               Louisiana  |   .1313877   .2318496     0.57   0.571    -.3230293    .5858046
                   Maine  |   .1556384   .2638504     0.59   0.555     -.361499    .6727758
                Maryland  |  -.0964685   .2334477    -0.41   0.679    -.5540176    .3610807
           Massachusetts  |  -.1018203   .2063154    -0.49   0.622    -.5061909    .3025504
                Michigan  |  -.0064264   .1857407    -0.03   0.972    -.3704715    .3576186
               Minnesota  |   -.322597   .2187545    -1.47   0.140    -.7513479    .1061538
             Mississippi  |  -.0315615   .2228487    -0.14   0.887    -.4683368    .4052138
                Missouri  |  -.0115922   .2067271    -0.06   0.955    -.4167698    .3935854
                 Montana  |  -.3877357   .3369553    -1.15   0.250    -1.048156    .2726845
                Nebraska  |  -.1012672   .2687367    -0.38   0.706    -.6279813     .425447
                  Nevada  |   -.154322   .2429318    -0.64   0.525    -.6304597    .3218156
           New Hampshire  |   .1575955   .2233996     0.71   0.481    -.2802595    .5954506
              New Jersey  |   .0965152   .1978763     0.49   0.626    -.2913151    .4843456
              New Mexico  |  -.1456843   .2417593    -0.60   0.547    -.6195239    .3281552
                New York  |   .2003545   .1827343     1.10   0.273    -.1577981    .5585072
          North Carolina  |   .0325664   .1936398     0.17   0.866    -.3469607    .4120935
            North Dakota  |  -.6013458   .3184225    -1.89   0.059    -1.225442    .0227509
                    Ohio  |  -.0730284   .1895951    -0.39   0.700     -.444628    .2985712
                Oklahoma  |    .069749   .2151441     0.32   0.746    -.3519256    .4914236
                  Oregon  |  -.0681257   .2179999    -0.31   0.755    -.4953977    .3591463
            Pennsylvania  |   .1336385   .1827512     0.73   0.465    -.2245472    .4918242
            Rhode Island  |  -.1396713   .3721926    -0.38   0.707    -.8691554    .5898127
          South Carolina  |  -.0460771    .210594    -0.22   0.827    -.4588338    .3666796
            South Dakota  |  -.2616991     .31703    -0.83   0.409    -.8830665    .3596683
               Tennessee  |   .3184491   .2004079     1.59   0.112    -.0743431    .7112414
                   Texas  |   .1735783   .1830101     0.95   0.343    -.1851148    .5322715
                    Utah  |   .2086482   .2342527     0.89   0.373    -.2504786     .667775
                 Vermont  |  -.2249939   .2933004    -0.77   0.443    -.7998522    .3498644
                Virginia  |   -.009068   .1969009    -0.05   0.963    -.3949867    .3768506
              Washington  |  -.1563428   .1929026    -0.81   0.418    -.5344248    .2217393
           West Virginia  |   .0511814   .2490731     0.21   0.837    -.4369929    .5393558
               Wisconsin  |   .0435615    .196174     0.22   0.824    -.3409324    .4280555
                 Wyoming  |    .828362   .9516251     0.87   0.384    -1.036789    2.693513
                          |
                    rural |
                       2  |  -.0463954   .0497966    -0.93   0.351     -.143995    .0512041
                       3  |   .1035453   .0574445     1.80   0.071    -.0090438    .2161345
                       4  |   .0510386   .0780104     0.65   0.513     -.101859    .2039363
                       5  |    .186865   .1184458     1.58   0.115    -.0452845    .4190144
                       6  |   .1245778   .0865544     1.44   0.150    -.0450658    .2942213
                       7  |   .0444156   .1020932     0.44   0.664    -.1556835    .2445146
                       8  |   -.035923   .2003323    -0.18   0.858    -.4285671     .356721
                       9  |   .1817512   .1956608     0.93   0.353    -.2017368    .5652393
                          |
                    _cons |   3.595886   .5031241     7.15   0.000     2.609781    4.581991
--------------------------+----------------------------------------------------------------
                  sigma_u |  .64369041
                  sigma_e |  .87037667
                      rho |  .35356247   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. esttab cates_w2to5, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                       banentry_w                
-------------------------------------------------
Brazil                      0.085         (0.516)
Great Britain              -0.420         (0.552)
Date=3                      0.202         (0.472)
Date=4                     -0.564         (0.499)
Date=5                     -0.909         (0.805)
Brazil # Date=3            -0.212         (0.679)
Brazil # Date=4             0.439         (0.640)
Brazil # Date=5             0.135         (0.956)
Great Britain # Da~3       -0.596         (0.607)
Great Britain # Da~4        0.378         (0.693)
Great Britain # Da~5        0.819         (1.041)
Democrat                   -1.124*        (0.479)
Other                      -1.570**       (0.487)
Brazil # Democrat           0.803         (0.556)
Brazil # Other              1.187*        (0.575)
Great Britain # De~t        0.935         (0.586)
Great Britain # Ot~r        1.703**       (0.595)
Date=3 # Democrat           0.050         (0.507)
Date=3 # Other              0.391         (0.523)
Date=4 # Democrat           0.413         (0.540)
Date=4 # Other              0.468         (0.563)
Date=5 # Democrat           0.452         (0.836)
Date=5 # Other              0.211         (0.846)
Brazil # Date=3 # ~t        0.400         (0.731)
Brazil # Date=3 # ~r       -0.012         (0.760)
Brazil # Date=4 # ~t       -0.878         (0.704)
Brazil # Date=4 # ~r       -0.631         (0.742)
Brazil # Date=5 # ~t        0.492         (1.006)
Brazil # Date=5 # ~r        0.178         (1.032)
Great Britain # Da~a        0.352         (0.664)
Great Britain # Da~r       -0.004         (0.684)
Great Britain # Da~a       -0.429         (0.749)
Great Britain # Da~r       -0.593         (0.774)
Great Britain # Da~a       -0.323         (1.086)
Great Britain # Da~r        0.171         (1.115)
racialresentment            0.231*        (0.101)
Brazil # racialres~t       -0.070         (0.115)
Great Britain # ra~t       -0.107         (0.124)
Date=3 # racialres~t       -0.026         (0.105)
Date=4 # racialres~t        0.038         (0.110)
Date=5 # racialres~t        0.066         (0.181)
Brazil # Date=3 # ~e        0.017         (0.150)
Brazil # Date=4 # ~e       -0.140         (0.145)
Brazil # Date=5 # ~e       -0.013         (0.213)
Great Britain # Da~r        0.105         (0.139)
Great Britain # Da~r       -0.095         (0.156)
Great Britain # Da~r       -0.133         (0.235)
Democrat # racialr~t        0.192         (0.112)
Other # racialrese~t        0.273*        (0.111)
Brazil # Democrat ~t       -0.182         (0.136)
Brazil # Other # r~n       -0.265*        (0.133)
Great Britain # De~a       -0.165         (0.143)
Great Britain # Ot~e       -0.357**       (0.138)
Date=3 # Democrat ~t       -0.049         (0.123)
Date=3 # Other # r~n       -0.079         (0.120)
Date=4 # Democrat ~t       -0.211         (0.132)
Date=4 # Other # r~n       -0.107         (0.132)
Date=5 # Democrat ~t       -0.179         (0.198)
Date=5 # Other # r~n       -0.052         (0.194)
Brazil # Date=3 # ~c       -0.048         (0.177)
Brazil # Date=3 # ~l        0.013         (0.176)
Brazil # Date=4 # ~c        0.350         (0.180)
Brazil # Date=4 # ~l        0.173         (0.178)
Brazil # Date=5 # ~c        0.002         (0.243)
Brazil # Date=5 # ~l        0.017         (0.237)
Great Britain # Da~a       -0.003         (0.170)
Great Britain # Da~#        0.049         (0.162)
Great Britain # Da~a        0.216         (0.188)
Great Britain # Da~#        0.174         (0.184)
Great Britain # Da~a        0.210         (0.261)
Great Britain # Da~#       -0.048         (0.260)
30-                         0.146*        (0.066)
45-                         0.396***      (0.063)
65-                         0.365***      (0.067)
gender                     -0.063         (0.035)
white                      -0.159***      (0.045)
married                     0.033         (0.038)
High school graduate       -0.270**       (0.103)
Some college               -0.352***      (0.106)
2-year                     -0.308**       (0.110)
4-year                     -0.475***      (0.108)
Post-grad                  -0.547***      (0.114)
income=2                    0.094         (0.102)
income=3                   -0.036         (0.102)
income=4                   -0.080         (0.100)
income=5                   -0.136         (0.101)
income=6                   -0.062         (0.103)
income=7                   -0.065         (0.113)
income=8                   -0.102         (0.106)
income=9                   -0.204         (0.107)
income=10                  -0.135         (0.108)
income=11                  -0.209         (0.118)
income=12                  -0.205         (0.129)
income=13                  -0.182         (0.143)
income=14                  -0.185         (0.173)
income=15                  -0.988**       (0.358)
income=16                   0.197         (0.221)
income=17                  -0.135         (0.097)
Alaska                     -0.761*        (0.317)
Arizona                    -0.121         (0.206)
Arkansas                   -0.054         (0.220)
California                  0.004         (0.179)
Colorado                    0.206         (0.218)
Connecticut                 0.004         (0.262)
Delaware                    0.088         (0.337)
District of Columbia       -0.193         (0.294)
Florida                     0.130         (0.181)
Georgia                    -0.033         (0.187)
Hawaii                     -0.013         (0.303)
Idaho                       0.386         (0.228)
Illinois                    0.016         (0.196)
Indiana                    -0.015         (0.209)
Iowa                        0.433         (0.246)
Kansas                      0.139         (0.243)
Kentucky                   -0.030         (0.215)
Louisiana                   0.131         (0.232)
Maine                       0.156         (0.264)
Maryland                   -0.096         (0.233)
Massachusetts              -0.102         (0.206)
Michigan                   -0.006         (0.186)
Minnesota                  -0.323         (0.219)
Mississippi                -0.032         (0.223)
Missouri                   -0.012         (0.207)
Montana                    -0.388         (0.337)
Nebraska                   -0.101         (0.269)
Nevada                     -0.154         (0.243)
New Hampshire               0.158         (0.223)
New Jersey                  0.097         (0.198)
New Mexico                 -0.146         (0.242)
New York                    0.200         (0.183)
North Carolina              0.033         (0.194)
North Dakota               -0.601         (0.318)
Ohio                       -0.073         (0.190)
Oklahoma                    0.070         (0.215)
Oregon                     -0.068         (0.218)
Pennsylvania                0.134         (0.183)
Rhode Island               -0.140         (0.372)
South Carolina             -0.046         (0.211)
South Dakota               -0.262         (0.317)
Tennessee                   0.318         (0.200)
Texas                       0.174         (0.183)
Utah                        0.209         (0.234)
Vermont                    -0.225         (0.293)
Virginia                   -0.009         (0.197)
Washington                 -0.156         (0.193)
West Virginia               0.051         (0.249)
Wisconsin                   0.044         (0.196)
Wyoming                     0.828         (0.952)
Rural Zip Code Ind~2       -0.046         (0.050)
Rural Zip Code Ind~3        0.104         (0.057)
Rural Zip Code Ind~4        0.051         (0.078)
Rural Zip Code Ind~5        0.187         (0.118)
Rural Zip Code Ind~6        0.125         (0.087)
Rural Zip Code Ind~7        0.044         (0.102)
Rural Zip Code Ind~8       -0.036         (0.200)
Rural Zip Code Ind~9        0.182         (0.196)
Constant                    3.596***      (0.503)
-------------------------------------------------
Observations                 6997                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_w2to5 using "tables/S7.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S7.csv)

. 
. margins banentryexp_w if democrat==0 & racialresentment <= `race_median', at(wave=(2 3 4 5)) 
> ///
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Republ
> icans") name(rep1, replace) ylabel(1(1)5)) 

Predictive margins                                         Number of obs = 353
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |   3.889511   .1979291    19.65   0.000     3.501577    4.277445
        1#Brazil  |   3.791379   .1213515    31.24   0.000     3.553535    4.029224
 1#Great Britain  |   3.191697   .1419781    22.48   0.000     2.913425    3.469969
         2#China  |   4.022926   .1192148    33.75   0.000      3.78927    4.256583
        2#Brazil  |   3.757399   .1516927    24.77   0.000     3.460086    4.054711
 2#Great Britain  |   3.002461   .1234821    24.31   0.000     2.760441    3.244482
         3#China  |     3.4257   .1984806    17.26   0.000     3.036686    3.814715
        3#Brazil  |    3.40231   .1319632    25.78   0.000     3.143667    3.660953
 3#Great Britain  |   2.859319   .1507818    18.96   0.000     2.563792    3.154846
         4#China  |   3.151752   .2460521    12.81   0.000     2.669499    3.634005
        4#Brazil  |   3.154012   .2148959    14.68   0.000     2.732824      3.5752
 4#Great Britain  |    2.92758   .2453427    11.93   0.000     2.446717    3.408443
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. margins banentryexp_w if democrat==0 & racialresentment > `race_median', at(wave=(2 3 4 5)) /
> //
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Republ
> icans") name(rep2, replace) ylabel(1(1)5))

Predictive margins                                       Number of obs = 1,561
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |    4.39072   .0642159    68.37   0.000     4.264859    4.516581
        1#Brazil  |   4.149036   .0606063    68.46   0.000      4.03025    4.267823
 1#Great Britain  |   3.474461   .0828107    41.96   0.000     3.312155    3.636767
         2#China  |    4.47022   .0496273    90.08   0.000     4.372952    4.567487
        2#Brazil  |   4.095816   .0668948    61.23   0.000     3.964704    4.226927
 2#Great Britain  |   3.445803   .0825981    41.72   0.000     3.283914    3.607693
         3#China  |   4.005663   .0721329    55.53   0.000     3.864285    4.147041
        3#Brazil  |   3.552046   .0887447    40.03   0.000      3.37811    3.725983
 3#Great Britain  |    3.02706   .0819739    36.93   0.000     2.866394    3.187725
         4#China  |    3.78766   .1047573    36.16   0.000      3.58234    3.992981
        4#Brazil  |   3.619302   .0872003    41.51   0.000     3.448393    3.790211
 4#Great Britain  |   3.073327   .1062297    28.93   0.000     2.865121    3.281534
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. 
. 
. margins banentryexp_w if democrat==1 & racialresentment <= `race_median', at(wave=(2 3 4 5)) 
> ///
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Democr
> ats") name(dem1, replace) ylabel(1(1)5))

Predictive margins                                       Number of obs = 2,024
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |   2.903672   .0777143    37.36   0.000     2.751355    3.055989
        1#Brazil  |   3.320106   .0778013    42.67   0.000     3.167619    3.472594
 1#Great Britain  |   2.909764   .0704442    41.31   0.000     2.771696    3.047832
         2#China  |   3.014975   .0738167    40.84   0.000     2.870297    3.159653
        2#Brazil  |   3.562541   .0721091    49.40   0.000     3.421209    3.703872
 2#Great Britain  |   2.967396   .0663644    44.71   0.000     2.837324    3.097468
         3#China  |   2.429511   .0832065    29.20   0.000      2.26643    2.592593
        3#Brazil  |   2.800419   .0729265    38.40   0.000     2.657486    2.943353
 3#Great Britain  |   2.612273   .0770273    33.91   0.000     2.461302    2.763243
         4#China  |   2.234559   .0921842    24.24   0.000     2.053882    2.415237
        4#Brazil  |   3.257145   .0852613    38.20   0.000     3.090036    3.424254
 4#Great Britain  |   2.882406   .0850587    33.89   0.000     2.715694    3.049118
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. margins banentryexp_w if democrat==1 & racialresentment > `race_median', at(wave=(2 3 4 5)) /
> //
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Democr
> ats") name(dem2, replace) ylabel(1(1)5))

Predictive margins                                         Number of obs = 633
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |    4.07021   .1123796    36.22   0.000      3.84995     4.29047
        1#Brazil  |   3.844478   .1242588    30.94   0.000     3.600935    4.088021
 1#Great Britain  |   3.383221   .1330869    25.42   0.000     3.122375    3.644066
         2#China  |    3.99036    .120147    33.21   0.000     3.754876    4.225844
        2#Brazil  |    3.81764   .1128228    33.84   0.000     3.596512    4.038769
 2#Great Britain  |   3.508358   .1128443    31.09   0.000     3.287187    3.729529
         3#China  |   3.155921   .1394372    22.63   0.000     2.882629    3.429213
        3#Brazil  |   3.420628   .1368189    25.00   0.000     3.152468    3.688789
 3#Great Britain  |   2.955679   .1380289    21.41   0.000     2.685147    3.226211
         4#China  |   3.112237   .1598022    19.48   0.000      2.79903    3.425443
        4#Brazil  |    3.46388     .15318    22.61   0.000     3.163653    3.764107
 4#Great Britain  |   3.265119    .128128    25.48   0.000     3.013992    3.516245
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. 
. 
. margins banentryexp_w if democrat==2 & racialresentment <= `race_median', at(wave=(2 3 4 5)) 
> ///
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Others
> ") name(oth1, replace) ylabel(1(1)5))

Predictive margins                                       Number of obs = 1,288
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |   2.715056    .089675    30.28   0.000     2.539296    2.890815
        1#Brazil  |   3.281071   .0982433    33.40   0.000     3.088517    3.473624
 1#Great Britain  |    3.02124   .0739679    40.85   0.000     2.876266    3.166215
         2#China  |   3.087007   .0890105    34.68   0.000     2.912549    3.261464
        2#Brazil  |   3.492397   .0950804    36.73   0.000     3.306043    3.678751
 2#Great Britain  |   3.116512   .0906281    34.39   0.000     2.938884    3.294139
         3#China  |   2.475228   .1089737    22.71   0.000     2.261644    2.688813
        3#Brazil  |   2.920269   .0972211    30.04   0.000      2.72972    3.110819
 3#Great Britain  |   2.733045   .0982066    27.83   0.000     2.540563    2.925526
         4#China  |   2.044703   .1060577    19.28   0.000     1.836833    2.252572
        4#Brazil  |   2.931707    .122099    24.01   0.000     2.692397    3.171016
 4#Great Britain  |   2.959359   .1153798    25.65   0.000     2.733218    3.185499
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. margins banentryexp_w if democrat==2 & racialresentment > `race_median', at(wave=(2 3 4 5)) /
> //
>         plot(recastci(rarea) ciopts(color(gs12)) legend(rows(1)) xtitle("Wave") title("Others
> ") name(oth2, replace) ylabel(1(1)5))

Predictive margins                                       Number of obs = 1,138
Model VCE: Robust

Expression: Linear prediction, predict()
1._at: wave = 2
2._at: wave = 3
3._at: wave = 4
4._at: wave = 5

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   std. err.      z    P>|z|     [95% conf. interval]
------------------+----------------------------------------------------------------
_at#banentryexp_w |
         1#China  |   4.027907   .0788046    51.11   0.000     3.873453    4.182361
        1#Brazil  |   3.763296   .0827615    45.47   0.000     3.601087    3.925506
 1#Great Britain  |   3.184425   .0857662    37.13   0.000     3.016327    3.352524
         2#China  |   4.139429   .0838476    49.37   0.000     3.975091    4.303767
        2#Brazil  |   3.787904   .0803783    47.13   0.000     3.630366    3.945443
 2#Great Britain  |    3.39937   .0876997    38.76   0.000     3.227482    3.571258
         3#China  |   3.618551   .1156007    31.30   0.000     3.391977    3.845124
        3#Brazil  |   3.316152   .1004403    33.02   0.000     3.119292    3.513011
 3#Great Britain  |   2.923041   .0954052    30.64   0.000      2.73605    3.110031
         4#China  |   3.390506   .1237105    27.41   0.000     3.148038    3.632974
        4#Brazil  |   3.456039    .105084    32.89   0.000     3.250078       3.662
 4#Great Britain  |   2.706156   .1236625    21.88   0.000     2.463782     2.94853
-----------------------------------------------------------------------------------

Variables that uniquely identify margins: wave banentryexp_w

. 
. grc1leg rep1 dem1 oth1, rows(1) name(low, replace) title("Low RR")

. grc1leg rep2 dem2 oth2, rows(1) name(high, replace) title("High RR")

. grc1leg low high, rows(2)

. 
. 
. graph export "figures/3.png", as(png) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/3.png saved as
    PNG format

. graph export "figures/3.pdf", as(pdf) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/3.pdf saved as
    PDF format

. 
. restore 

. 
. 
. 
. 
. 
. preserve 

. 
. keep banentryexp_w* banentry_w* democrat_w* caseid weight* aia racialresentment agecat gender
>  white married educ income inputstate rural

. drop banentryexp_w1 banentryexp_w2 banentry_w1 banentry_w2

. drop weight

. 
. reshape long banentryexp_w banentry_w democrat_w weight_w, i(caseid) j(wave)
(j = 1 2 3 4 5 6)
(variable banentryexp_w1 not found)
(variable banentry_w1 not found)
(variable banentryexp_w2 not found)
(variable banentry_w2 not found)
weight_w2:  2401 values would be changed; not changed
weight_w3:  2104 values would be changed; not changed
weight_w4:  1949 values would be changed; not changed
weight_w5:  1871 values would be changed; not changed
weight_w6:  3000 values would be changed; not changed

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            4,350   ->   26,100      
Number of variables                  31   ->   16          
j variable (6 values)                     ->   wave
xij variables:
banentryexp_w1 banentryexp_w2 ... banentryexp_w6->banentryexp_w
banentry_w1 banentry_w2 ... banentry_w6   ->   banentry_w
democrat_w1 democrat_w2 ... democrat_w6   ->   democrat_w
      weight_w1 weight_w2 ... weight_w6   ->   weight_w
-----------------------------------------------------------------------------

. label values democrat democrat

. 
. xtset caseid wave

Panel variable: caseid (strongly balanced)
 Time variable: wave, 1 to 6
         Delta: 1 unit

. gen  treatwaveid = wave*10+banentryexp_w
(17,176 missing values generated)

. sort treatwaveid

. merge m:1 treatwaveid using country_data.dta
(variable banentryexp_w was long, now double to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                        17,176
        from master                    17,176  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             8,924  (_merge==3)
    -----------------------------------------

. drop if wave==.
(0 observations deleted)

. drop _merge

. 
. 
. graph drop _all

. 
. 
. tempvar logcases

. gen `logcases' = ln(new_cases_smoothed)
(17,176 missing values generated)

. 
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs              18,000
25%            2              1       Sum of wgt.      18,000

50%            3                      Mean              3.181
                        Largest       Std. dev.      1.459803
75%            5              5
90%            5              5       Variance       2.131024
95%            5              5       Skewness      -.2031789
99%            5              5       Kurtosis        1.70661

. local race_median = r(p50)

. 
. 
. eststo cates_cases_w2to5: xtreg banentry_w i.banentryexp_w##i.democrat_w##c.`logcases'##c.rac
> ialresentment  i.agecat gender white married i.educ i.income i.inputstate i.rural, re robust

Random-effects GLS regression                   Number of obs     =      6,997
Group variable: caseid                          Number of groups  =      2,300

R-squared:                                      Obs per group:
     Within  = 0.1118                                         min =          1
     Between = 0.2873                                         avg =        3.0
     Overall = 0.2248                                         max =          4

                                                Wald chi2(120)    =    1674.87
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                          (Std. err. adjusted for 2,300 clusters in caseid)
-------------------------------------------------------------------------------------------
                          |               Robust
               banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
            banentryexp_w |
                  Brazil  |   4.803001   3.451473     1.39   0.164    -1.961762    11.56776
           Great Britain  |  -.0430935   1.205637    -0.04   0.971    -2.406098    2.319911
                          |
               democrat_w |
                Democrat  |  -.8157501    .593335    -1.37   0.169    -1.978665    .3471651
                   Other  |  -1.663969   .6144993    -2.71   0.007    -2.868365    -.459572
                          |
 banentryexp_w#democrat_w |
         Brazil#Democrat  |  -9.774731   3.791615    -2.58   0.010    -17.20616   -2.343302
            Brazil#Other  |  -3.674479   4.041954    -0.91   0.363    -11.59656    4.247605
  Great Britain#Democrat  |   1.449088   1.303879     1.11   0.266    -1.106468    4.004643
     Great Britain#Other  |   2.683998   1.354548     1.98   0.048     .0291331    5.338863
                          |
                 __000000 |   .1570906   .1596704     0.98   0.325    -.1558577    .4700389
                          |
 banentryexp_w#c.__000000 |
                  Brazil  |  -.5590827   .3747545    -1.49   0.136    -1.293588    .1754227
           Great Britain  |  -.1394358   .2102448    -0.66   0.507    -.5515081    .2726365
                          |
    democrat_w#c.__000000 |
                Democrat  |   -.018384   .1746263    -0.11   0.916    -.3606452    .3238772
                   Other  |   .1495062   .1827951     0.82   0.413    -.2087656     .507778
                          |
 banentryexp_w#democrat_w#|
               c.__000000 |
         Brazil#Democrat  |   1.018682    .408265     2.50   0.013     .2184969    1.818866
            Brazil#Other  |   .3351318   .4380665     0.77   0.444    -.5234627    1.193726
  Great Britain#Democrat  |  -.0681268    .228803    -0.30   0.766    -.5165724    .3803189
     Great Britain#Other  |  -.2432998   .2390874    -1.02   0.309    -.7119025    .2253029
                          |
         racialresentment |   .2976175   .1236222     2.41   0.016     .0553224    .5399127
                          |
            banentryexp_w#|
       c.racialresentment |
                  Brazil  |  -1.026136   .7711728    -1.33   0.183    -2.537607    .4853344
           Great Britain  |   .1667244   .2764857     0.60   0.547    -.3751776    .7086264
                          |
               democrat_w#|
       c.racialresentment |
                Democrat  |   .0898986   .1447489     0.62   0.535     -.193804    .3736012
                   Other  |    .277073   .1434362     1.93   0.053    -.0040567    .5582028
                          |
 banentryexp_w#democrat_w#|
       c.racialresentment |
         Brazil#Democrat  |   2.190067   .9741302     2.25   0.025     .2808074    4.099328
            Brazil#Other  |   .6896009   .9578334     0.72   0.472    -1.187718     2.56692
  Great Britain#Democrat  |  -.1972418   .3318955    -0.59   0.552    -.8477451    .4532614
     Great Britain#Other  |  -.5230371   .3250825    -1.61   0.108    -1.160187    .1141129
                          |
               c.__000000#|
       c.racialresentment |  -.0182429   .0356501    -0.51   0.609    -.0881157      .05163
                          |
 banentryexp_w#c.__000000#|
       c.racialresentment |
                  Brazil  |   .1018779   .0835681     1.22   0.223    -.0619126    .2656683
           Great Britain  |  -.0247176   .0474787    -0.52   0.603    -.1177741    .0683388
                          |
    democrat_w#c.__000000#|
       c.racialresentment |
                Democrat  |  -.0062246   .0436579    -0.14   0.887    -.0917926    .0793434
                   Other  |  -.0272515   .0429665    -0.63   0.526    -.1114643    .0569613
                          |
 banentryexp_w#democrat_w#|
               c.__000000#|
       c.racialresentment |
         Brazil#Democrat  |  -.2135444   .1039965    -2.05   0.040    -.4173737   -.0097151
            Brazil#Other  |  -.0648812   .1031902    -0.63   0.530    -.2671302    .1373678
  Great Britain#Democrat  |   .0218466   .0574539     0.38   0.704    -.0907611    .1344543
     Great Britain#Other  |   .0461168   .0568926     0.81   0.418    -.0653906    .1576242
                          |
                   agecat |
                     30-  |   .1342469   .0659421     2.04   0.042     .0050027     .263491
                     45-  |   .3728912   .0629188     5.93   0.000     .2495726    .4962098
                     65-  |   .3325806   .0668386     4.98   0.000     .2015794    .4635818
                          |
                   gender |  -.0517045   .0356475    -1.45   0.147    -.1215724    .0181633
                    white |  -.1703935   .0449415    -3.79   0.000    -.2584772   -.0823098
                  married |   .0373233   .0384554     0.97   0.332    -.0380479    .1126945
                          |
                     educ |
    High school graduate  |  -.2620689   .1004119    -2.61   0.009    -.4588727   -.0652651
            Some college  |  -.3378123   .1033678    -3.27   0.001    -.5404094   -.1352151
                  2-year  |  -.3046168   .1084788    -2.81   0.005    -.5172314   -.0920023
                  4-year  |  -.4731327   .1050883    -4.50   0.000    -.6791019   -.2671635
               Post-grad  |  -.5446671   .1121178    -4.86   0.000     -.764414   -.3249203
                          |
                   income |
                       2  |   .0952636   .1028946     0.93   0.355    -.1064062    .2969334
                       3  |  -.0475191   .1021848    -0.47   0.642    -.2477977    .1527595
                       4  |  -.0711842   .1014544    -0.70   0.483    -.2700311    .1276626
                       5  |  -.1331447   .1024919    -1.30   0.194     -.334025    .0677356
                       6  |  -.0673893   .1045759    -0.64   0.519    -.2723543    .1375757
                       7  |  -.0818996   .1143078    -0.72   0.474    -.3059389    .1421396
                       8  |  -.1147496   .1058295    -1.08   0.278    -.3221716    .0926724
                       9  |  -.2010176   .1070836    -1.88   0.060    -.4108977    .0088625
                      10  |  -.1307601   .1092674    -1.20   0.231    -.3449202       .0834
                      11  |  -.2036156     .11882    -1.71   0.087    -.4364985    .0292673
                      12  |  -.2075439   .1307233    -1.59   0.112    -.4637569     .048669
                      13  |  -.1862972   .1473866    -1.26   0.206    -.4751697    .1025753
                      14  |  -.2167172   .1777879    -1.22   0.223    -.5651752    .1317408
                      15  |  -1.001526   .4102522    -2.44   0.015    -1.805605   -.1974464
                      16  |   .2173549   .2334365     0.93   0.352    -.2401722    .6748821
                      17  |  -.1370799   .0975291    -1.41   0.160    -.3282333    .0540736
                          |
               inputstate |
                  Alaska  |  -.7678986   .3096782    -2.48   0.013    -1.374857   -.1609404
                 Arizona  |  -.0901509   .2024262    -0.45   0.656     -.486899    .3065972
                Arkansas  |  -.0151299   .2199768    -0.07   0.945    -.4462764    .4160166
              California  |   .0285652   .1750923     0.16   0.870    -.3146095    .3717399
                Colorado  |   .2530451   .2141747     1.18   0.237    -.1667295    .6728198
             Connecticut  |   .0216709   .2583681     0.08   0.933    -.4847212     .528063
                Delaware  |   .1262503    .313713     0.40   0.687    -.4886159    .7411165
    District of Columbia  |  -.2703308   .3096723    -0.87   0.383    -.8772772    .3366157
                 Florida  |    .156361   .1764899     0.89   0.376    -.1895528    .5022748
                 Georgia  |  -.0156026   .1829832    -0.09   0.932    -.3742431     .343038
                  Hawaii  |   .0543897   .2930096     0.19   0.853    -.5198985    .6286779
                   Idaho  |   .3834141   .2164178     1.77   0.076    -.0407571    .8075852
                Illinois  |   .0352269   .1913041     0.18   0.854    -.3397222     .410176
                 Indiana  |   .0504691   .2055128     0.25   0.806    -.3523286    .4532668
                    Iowa  |   .4529947   .2471995     1.83   0.067    -.0315074    .9374968
                  Kansas  |   .1675136   .2304527     0.73   0.467    -.2841654    .6191927
                Kentucky  |   .0244028   .2154284     0.11   0.910    -.3978292    .4466347
               Louisiana  |   .1582288   .2337163     0.68   0.498    -.2998467    .6163043
                   Maine  |   .1290545   .2686764     0.48   0.631    -.3975415    .6556505
                Maryland  |  -.1102158   .2273803    -0.48   0.628    -.5558729    .3354414
           Massachusetts  |  -.0877141   .2053285    -0.43   0.669    -.4901505    .3147223
                Michigan  |   .0185579   .1813566     0.10   0.918    -.3368944    .3740103
               Minnesota  |  -.2995747   .2146914    -1.40   0.163    -.7203621    .1212128
             Mississippi  |  -.0371462    .219612    -0.17   0.866    -.4675778    .3932854
                Missouri  |   .0023476    .203957     0.01   0.991    -.3974007    .4020959
                 Montana  |  -.3376702   .3371759    -1.00   0.317    -.9985229    .3231824
                Nebraska  |  -.0686632   .2741975    -0.25   0.802    -.6060805     .468754
                  Nevada  |  -.1509704   .2404453    -0.63   0.530    -.6222345    .3202937
           New Hampshire  |   .2020115   .2122762     0.95   0.341    -.2140423    .6180652
              New Jersey  |   .1359567   .1937619     0.70   0.483    -.2438096     .515723
              New Mexico  |  -.1537756   .2486771    -0.62   0.536    -.6411737    .3336226
                New York  |    .230157   .1788024     1.29   0.198    -.1202894    .5806033
          North Carolina  |   .0551134   .1912378     0.29   0.773    -.3197058    .4299327
            North Dakota  |   -.577713   .3208409    -1.80   0.072     -1.20655    .0511236
                    Ohio  |  -.0745049   .1860271    -0.40   0.689    -.4391113    .2901015
                Oklahoma  |   .0842959   .2090954     0.40   0.687    -.3255235    .4941154
                  Oregon  |  -.0612167   .2156359    -0.28   0.776    -.4838554     .361422
            Pennsylvania  |   .1716957   .1786106     0.96   0.336    -.1783747     .521766
            Rhode Island  |  -.0713552   .3674064    -0.19   0.846    -.7914586    .6487482
          South Carolina  |  -.0236013   .2059415    -0.11   0.909    -.4272392    .3800366
            South Dakota  |  -.2281091   .3250996    -0.70   0.483    -.8652926    .4090743
               Tennessee  |   .3458068   .1965192     1.76   0.078    -.0393637    .7309773
                   Texas  |   .1748429   .1789804     0.98   0.329    -.1759522     .525638
                    Utah  |   .2130369   .2289865     0.93   0.352    -.2357684    .6618421
                 Vermont  |  -.1436955   .2929016    -0.49   0.624     -.717772    .4303811
                Virginia  |   .0236287    .194205     0.12   0.903    -.3570061    .4042635
              Washington  |  -.1435108   .1899434    -0.76   0.450    -.5157929    .2287714
           West Virginia  |   .1164226   .2429692     0.48   0.632    -.3597883    .5926334
               Wisconsin  |   .0644749   .1915129     0.34   0.736    -.3108835    .4398333
                 Wyoming  |    .614746   .9666303     0.64   0.525    -1.279815    2.509307
                          |
                    rural |
                       2  |  -.0510633   .0499387    -1.02   0.307    -.1489414    .0468148
                       3  |   .0892406   .0574496     1.55   0.120    -.0233586    .2018398
                       4  |   .0507146   .0782747     0.65   0.517     -.102701    .2041302
                       5  |   .1741833   .1210595     1.44   0.150     -.063089    .4114556
                       6  |   .0908206    .089068     1.02   0.308    -.0837495    .2653907
                       7  |   .0360313   .1014775     0.36   0.723     -.162861    .2349237
                       8  |  -.0381199     .19976    -0.19   0.849    -.4296424    .3534025
                       9  |   .1618842   .1981632     0.82   0.414    -.2265086     .550277
                          |
                    _cons |   2.889506    .585972     4.93   0.000     1.741022     4.03799
--------------------------+----------------------------------------------------------------
                  sigma_u |  .63614075
                  sigma_e |   .9001717
                      rho |  .33307023   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. esttab cates_cases_w2to5, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                       banentry_w                
-------------------------------------------------
Brazil                      4.803         (3.451)
Great Britain              -0.043         (1.206)
Democrat                   -0.816         (0.593)
Other                      -1.664**       (0.614)
Brazil # Democrat          -9.775**       (3.792)
Brazil # Other             -3.674         (4.042)
Great Britain # De~t        1.449         (1.304)
Great Britain # Ot~r        2.684*        (1.355)
__000000                    0.157         (0.160)
Brazil # __000000          -0.559         (0.375)
Great Britain~000000       -0.139         (0.210)
Democrat # __000000        -0.018         (0.175)
Other # __000000            0.150         (0.183)
Brazil # Demo~000000        1.019*        (0.408)
Brazil # Othe~000000        0.335         (0.438)
Great Britain # ~000       -0.068         (0.229)
Great Britain~000000       -0.243         (0.239)
racialresentment            0.298*        (0.124)
Brazil # racialres~t       -1.026         (0.771)
Great Britain # ra~t        0.167         (0.276)
Democrat # racialr~t        0.090         (0.145)
Other # racialrese~t        0.277         (0.143)
Brazil # Democrat ~t        2.190*        (0.974)
Brazil # Other # r~n        0.690         (0.958)
Great Britain # De~a       -0.197         (0.332)
Great Britain # Ot~e       -0.523         (0.325)
__000000 # racialr~t       -0.018         (0.036)
Brazil # __000000 ~t        0.102         (0.084)
Great Britain # __~a       -0.025         (0.047)
Democrat # __00000~e       -0.006         (0.044)
Other # __000000 #~m       -0.027         (0.043)
Brazil # Democrat ~r       -0.214*        (0.104)
Brazil # Other # _~i       -0.065         (0.103)
Great Britain # ~000        0.022         (0.057)
Great Britain~000000        0.046         (0.057)
30-                         0.134*        (0.066)
45-                         0.373***      (0.063)
65-                         0.333***      (0.067)
gender                     -0.052         (0.036)
white                      -0.170***      (0.045)
married                     0.037         (0.038)
High school graduate       -0.262**       (0.100)
Some college               -0.338**       (0.103)
2-year                     -0.305**       (0.108)
4-year                     -0.473***      (0.105)
Post-grad                  -0.545***      (0.112)
income=2                    0.095         (0.103)
income=3                   -0.048         (0.102)
income=4                   -0.071         (0.101)
income=5                   -0.133         (0.102)
income=6                   -0.067         (0.105)
income=7                   -0.082         (0.114)
income=8                   -0.115         (0.106)
income=9                   -0.201         (0.107)
income=10                  -0.131         (0.109)
income=11                  -0.204         (0.119)
income=12                  -0.208         (0.131)
income=13                  -0.186         (0.147)
income=14                  -0.217         (0.178)
income=15                  -1.002*        (0.410)
income=16                   0.217         (0.233)
income=17                  -0.137         (0.098)
Alaska                     -0.768*        (0.310)
Arizona                    -0.090         (0.202)
Arkansas                   -0.015         (0.220)
California                  0.029         (0.175)
Colorado                    0.253         (0.214)
Connecticut                 0.022         (0.258)
Delaware                    0.126         (0.314)
District of Columbia       -0.270         (0.310)
Florida                     0.156         (0.176)
Georgia                    -0.016         (0.183)
Hawaii                      0.054         (0.293)
Idaho                       0.383         (0.216)
Illinois                    0.035         (0.191)
Indiana                     0.050         (0.206)
Iowa                        0.453         (0.247)
Kansas                      0.168         (0.230)
Kentucky                    0.024         (0.215)
Louisiana                   0.158         (0.234)
Maine                       0.129         (0.269)
Maryland                   -0.110         (0.227)
Massachusetts              -0.088         (0.205)
Michigan                    0.019         (0.181)
Minnesota                  -0.300         (0.215)
Mississippi                -0.037         (0.220)
Missouri                    0.002         (0.204)
Montana                    -0.338         (0.337)
Nebraska                   -0.069         (0.274)
Nevada                     -0.151         (0.240)
New Hampshire               0.202         (0.212)
New Jersey                  0.136         (0.194)
New Mexico                 -0.154         (0.249)
New York                    0.230         (0.179)
North Carolina              0.055         (0.191)
North Dakota               -0.578         (0.321)
Ohio                       -0.075         (0.186)
Oklahoma                    0.084         (0.209)
Oregon                     -0.061         (0.216)
Pennsylvania                0.172         (0.179)
Rhode Island               -0.071         (0.367)
South Carolina             -0.024         (0.206)
South Dakota               -0.228         (0.325)
Tennessee                   0.346         (0.197)
Texas                       0.175         (0.179)
Utah                        0.213         (0.229)
Vermont                    -0.144         (0.293)
Virginia                    0.024         (0.194)
Washington                 -0.144         (0.190)
West Virginia               0.116         (0.243)
Wisconsin                   0.064         (0.192)
Wyoming                     0.615         (0.967)
Rural Zip Code Ind~2       -0.051         (0.050)
Rural Zip Code Ind~3        0.089         (0.057)
Rural Zip Code Ind~4        0.051         (0.078)
Rural Zip Code Ind~5        0.174         (0.121)
Rural Zip Code Ind~6        0.091         (0.089)
Rural Zip Code Ind~7        0.036         (0.101)
Rural Zip Code Ind~8       -0.038         (0.200)
Rural Zip Code Ind~9        0.162         (0.198)
Constant                    2.890***      (0.586)
-------------------------------------------------
Observations                 6997                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_cases_w2to5 using "tables/S8.csv", b(3) se(3) wide label nobaselevels csv replac
> e
(output written to tables/S8.csv)

. 
. 
. 
. margins if aia<=`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3) democrat==0) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Republicans") name(low_r, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 4,565
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 0
2._at: banentryexp_w = 2
       democrat_w    = 0
3._at: banentryexp_w = 3
       democrat_w    = 0

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .1068378   .0649923     1.64   0.100    -.0205448    .2342204
          2  |  -.1716065   .1414026    -1.21   0.225    -.4487504    .1055375
          3  |  -.1006866   .0526543    -1.91   0.056     -.203887    .0025139
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. margins if aia>`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3)  democrat==0) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Republicans") name(high_r, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 2,432
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 0
2._at: banentryexp_w = 2
       democrat_w    = 0
3._at: banentryexp_w = 3
       democrat_w    = 0

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .0810437   .0282313     2.87   0.004     .0257113     .136376
          2  |  -.0533523   .0770162    -0.69   0.488    -.2043014    .0975967
          3  |  -.1614297   .0298848    -5.40   0.000    -.2200029   -.1028566
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. 
. 
. margins if aia<=`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3) democrat==1) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Democrats") name(low_d, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 4,565
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 1
2._at: banentryexp_w = 2
       democrat_w    = 1
3._at: banentryexp_w = 3
       democrat_w    = 1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .0713071    .034331     2.08   0.038     .0040195    .1385946
          2  |   .2233033   .0735833     3.03   0.002     .0790828    .3675239
          3  |  -.1441643   .0279638    -5.16   0.000    -.1989723   -.0893563
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. margins if aia>`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3)  democrat==1) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Democrats") name(high_d, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 2,432
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 1
2._at: banentryexp_w = 2
       democrat_w    = 1
3._at: banentryexp_w = 3
       democrat_w    = 1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .0367117   .0543178     0.68   0.499    -.0697493    .1431727
          2  |   .0308193   .1230027     0.25   0.802    -.2102615    .2719001
          3  |  -.1828191    .047876    -3.82   0.000    -.2766543    -.088984
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. 
. 
. margins if aia<=`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3) democrat==2) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Others") name(low_o, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 4,565
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 2
2._at: banentryexp_w = 2
       democrat_w    = 2
3._at: banentryexp_w = 3
       democrat_w    = 2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .1812756   .0358928     5.05   0.000     .1109269    .2516242
          2  |   .0592378    .080091     0.74   0.460    -.0977376    .2162132
          3  |  -.1425127   .0294863    -4.83   0.000    -.2003049   -.0847206
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. margins if aia>`race_median', dydx(`logcases') at(banentryexp_w==(1 2 3)  democrat==2) plot(c
> iopts(lwidth(medium))  recast(scatter) ///
>         xtitle("") title("Others") name(high_o, replace) legend(off)  yline(0))

Average marginal effects                                 Number of obs = 2,432
Model VCE: Robust

Expression: Linear prediction, predict()
dy/dx wrt:  __000000
1._at: banentryexp_w = 1
       democrat_w    = 2
2._at: banentryexp_w = 2
       democrat_w    = 2
3._at: banentryexp_w = 3
       democrat_w    = 2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
__000000     |
         _at |
          1  |   .1169497   .0367221     3.18   0.001     .0449757    .1889237
          2  |   .0472228   .0791482     0.60   0.551    -.1079049    .2023504
          3  |  -.1765816   .0315646    -5.59   0.000    -.2384471   -.1147162
------------------------------------------------------------------------------

Variables that uniquely identify margins: banentryexp_w

. 
. graph combine low_r low_d low_o, rows(1) name(low, replace) title("Low RR") ycommon

. graph combine high_r high_d high_o, rows(1) name(high, replace) title("High RR") ycommon

. graph combine low high, rows(2) ycommon

. 
. graph export "figures/4.png", as(png) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/4.png saved as
    PNG format

. graph export "figures/4.pdf", as(pdf) replace
file /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/figures/4.pdf saved as
    PDF format

. 
. 
. 
. 
. 
. restore

. 
. 
. 
. 
. 
. ******* REPEAT THE WHOLE ANALYSIS FOR WHITE RESPONDENTS ONLY
. 
. use "survey.dta", clear

. 
. keep if white==1
(2,179 observations deleted)

. 
. gen aia = (iai*-1)+6
(2 missing values generated)

. 
. gen feelingthermometer = .
(2,171 missing values generated)

. replace feelingthermometer = (q137_4+q137_5+q137_6)/3 if race==1
(1,248 real changes made)

. replace feelingthermometer = (q137_3+q137_5+q137_6)/3 if race==2
(0 real changes made)

. replace feelingthermometer = (q137_3+q137_4+q137_6)/3 if race==3
(0 real changes made)

. replace feelingthermometer = feelingthermometer*-1
(1,248 real changes made)

. 
. 
. * validate RR measure: WHITES ONLY
. 
. eststo valid: reg feelingthermometer i.racialresentment, robust

Linear regression                               Number of obs     =      1,248
                                                F(4, 1243)        =      21.01
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0591
                                                Root MSE          =     21.341

----------------------------------------------------------------------------------
                 |               Robust
feelingthermom~r | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
racialresentment |
              2  |   3.020357   2.019591     1.50   0.135    -.9418271    6.982541
              3  |   10.02505   1.934479     5.18   0.000     6.229841    13.82025
              4  |   9.383509    1.81708     5.16   0.000     5.818627    12.94839
              5  |    15.0861   1.809439     8.34   0.000     11.53621    18.63599
                 |
           _cons |  -83.79658   1.348073   -62.16   0.000    -86.44133   -81.15183
----------------------------------------------------------------------------------

. esttab valid using "tables/S10.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S10.csv)

. 
. 
. 
. 
. * WAVE 1 BAN ENTRY EXPERIMENT: WHITES ONLY
. 
. 
. 
. gen entry_exp_recode = .
(2,171 missing values generated)

. replace entry_exp_recode = 1 if entry_exp==2
(715 real changes made)

. replace entry_exp_recode = 2 if entry_exp==1
(689 real changes made)

. replace entry_exp_recode = 3 if entry_exp==3
(767 real changes made)

. label define entry_exp_recode 1 "Britain" 2 "China" 3 "Italy"

. label values entry_exp_recode entry_exp_recode

. 
. summarize banentry_w1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 banentry_w1 |      2,165    3.876212    1.201283          1          5

. local mean_b = r(mean)

. 
. summarize aia, detail

                             aia
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%     1.333333              1       Obs               2,169
25%     1.666667              1       Sum of wgt.       2,169

50%     2.666667                      Mean           2.742278
                        Largest       Std. dev.      1.128628
75%     3.666667              5
90%     4.333333              5       Variance       1.273801
95%     4.666667              5       Skewness       .1717166
99%            5              5       Kurtosis       2.097237

. local aia_median = r(p50)

. 
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs               2,171
25%            2              1       Sum of wgt.       2,171

50%            3                      Mean           3.282819
                        Largest       Std. dev.      1.451259
75%            5              5
90%            5              5       Variance       2.106152
95%            5              5       Skewness      -.3005258
99%            5              5       Kurtosis       1.757661

. local race_median = r(p50)

. 
. 
. 
. * SATE estimates:  WHITES ONLY 
. eststo sate_w1: reg banentry_w1 i.entry_exp_recode  [pweight=weight], robust
(sum of wgt is 2,023.20888310687)

Linear regression                               Number of obs     =      2,165
                                                F(2, 2162)        =       7.93
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0073
                                                Root MSE          =      1.176

----------------------------------------------------------------------------------
                 |               Robust
     banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
entry_exp_recode |
          China  |   .1233898   .0666468     1.85   0.064    -.0073087    .2540882
          Italy  |   .2434519    .061138     3.98   0.000     .1235564    .3633474
                 |
           _cons |   3.790349   .0441599    85.83   0.000     3.703749    3.876949
----------------------------------------------------------------------------------

. eststo sate_w1_c: reg banentry_w1 i.entry_exp_recode  i.agecat gender white married i.educ i.
> income i.inputstate i.rural [pweight=weight], robust
(sum of wgt is 1,959.53164743084)
note: white omitted because of collinearity.

Linear regression                               Number of obs     =      2,093
                                                F(86, 2006)       =       3.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1054
                                                Root MSE          =     1.1363

---------------------------------------------------------------------------------------
                      |               Robust
          banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------+----------------------------------------------------------------
     entry_exp_recode |
               China  |   .1600717   .0662131     2.42   0.016     .0302181    .2899252
               Italy  |   .2873409   .0629638     4.56   0.000     .1638595    .4108223
                      |
               agecat |
                 30-  |   .0133551   .0984336     0.14   0.892    -.1796878     .206398
                 45-  |    .309512   .0925556     3.34   0.001     .1279967    .4910272
                 65-  |   .2232595   .0976158     2.29   0.022     .0318205    .4146985
                      |
               gender |   .0715134    .053025     1.35   0.178    -.0324765    .1755033
                white |          0  (omitted)
              married |   .0693371   .0576955     1.20   0.230    -.0438122    .1824865
                      |
                 educ |
High school graduate  |  -.2405695   .1572837    -1.53   0.126    -.5490261    .0678871
        Some college  |  -.4939012   .1611572    -3.06   0.002    -.8099542   -.1778481
              2-year  |  -.3908351   .1666692    -2.34   0.019    -.7176979   -.0639723
              4-year  |  -.7129175   .1631808    -4.37   0.000    -1.032939   -.3928959
           Post-grad  |   -.832031   .1714717    -4.85   0.000    -1.168312   -.4957498
                      |
               income |
                   2  |   .3187428    .174656     1.82   0.068    -.0237833     .661269
                   3  |   .2690081   .1658211     1.62   0.105    -.0561916    .5942077
                   4  |   .1735478   .1783963     0.97   0.331    -.1763137    .5234092
                   5  |   .3253904   .1733282     1.88   0.061    -.0145316    .6653124
                   6  |   .3714454   .1841381     2.02   0.044     .0103235    .7325673
                   7  |    .462815   .1829678     2.53   0.011     .1039883    .8216417
                   8  |   .3101183   .1824798     1.70   0.089    -.0477515    .6679881
                   9  |    .243783   .1833535     1.33   0.184    -.1158003    .6033662
                  10  |   .1064719    .189601     0.56   0.574    -.2653635    .4783074
                  11  |   .2871509   .1926141     1.49   0.136    -.0905937    .6648954
                  12  |   .2488405   .2061857     1.21   0.228    -.1555199     .653201
                  13  |   .2354127   .2437656     0.97   0.334    -.2426476     .713473
                  14  |   .1360067   .2861359     0.48   0.635    -.4251478    .6971613
                  15  |   .4639251   .2515103     1.84   0.065    -.0293236    .9571738
                  16  |   .7336284   .2421709     3.03   0.002     .2586956    1.208561
                  17  |   .3687079   .1670454     2.21   0.027     .0411072    .6963086
                      |
           inputstate |
              Alaska  |  -.1715855   .6889781    -0.25   0.803    -1.522773    1.179602
             Arizona  |   .1112025   .3113324     0.36   0.721    -.4993662    .7217712
            Arkansas  |   .5282658   .3247432     1.63   0.104    -.1086035    1.165135
          California  |  -.0232389   .2869039    -0.08   0.935    -.5858998    .5394219
            Colorado  |   .0646617   .3344731     0.19   0.847    -.5912892    .7206126
         Connecticut  |    .559898    .317845     1.76   0.078    -.0634428    1.183239
            Delaware  |  -.3861891   .4426592    -0.87   0.383    -1.254309    .4819308
District of Columbia  |   -.264685   .7662985    -0.35   0.730    -1.767509    1.238139
             Florida  |   .2325454   .2740223     0.85   0.396    -.3048527    .7699435
             Georgia  |   .4960849    .284938     1.74   0.082    -.0627205     1.05489
              Hawaii  |  -.1524393   .4158756    -0.37   0.714    -.9680326     .663154
               Idaho  |  -.3137415   .4571934    -0.69   0.493    -1.210365     .582882
            Illinois  |   .1156169    .294978     0.39   0.695    -.4628784    .6941122
             Indiana  |   .2165559   .3048208     0.71   0.478    -.3812426    .8143544
                Iowa  |   .4881657   .3231299     1.51   0.131    -.1455396    1.121871
              Kansas  |   .5800648   .3270023     1.77   0.076    -.0612349    1.221365
            Kentucky  |   .4065621   .2932334     1.39   0.166    -.1685117    .9816359
           Louisiana  |   .8198322   .2963811     2.77   0.006     .2385854    1.401079
               Maine  |   .4332137   .3837601     1.13   0.259    -.3193964    1.185824
            Maryland  |   .2492104   .3597264     0.69   0.489     -.456266    .9546869
       Massachusetts  |   .1027278   .3145914     0.33   0.744    -.5142322    .7196878
            Michigan  |   .2643356   .2877509     0.92   0.358    -.2999862    .8286574
           Minnesota  |   .0840711   .3235556     0.26   0.795     -.550469    .7186112
         Mississippi  |   .1243962   .3512048     0.35   0.723    -.5643682    .8131605
            Missouri  |   .3494856   .2876214     1.22   0.224    -.2145823    .9135535
             Montana  |  -.8145459   .4495004    -1.81   0.070    -1.696082    .0669906
            Nebraska  |  -.1200985   .5678695    -0.21   0.833    -1.233774    .9935772
              Nevada  |    .173358   .3527151     0.49   0.623    -.5183682    .8650842
       New Hampshire  |  -.0091882   .3564285    -0.03   0.979     -.708197    .6898206
          New Jersey  |   .3892268   .3164105     1.23   0.219    -.2313007    1.009754
          New Mexico  |   .3084562   .3349377     0.92   0.357     -.348406    .9653183
            New York  |   .2436233   .2820337     0.86   0.388    -.3094864     .796733
      North Carolina  |    .404782   .2903546     1.39   0.163    -.1646461      .97421
        North Dakota  |  -.9472832   .3924444    -2.41   0.016    -1.716924   -.1776418
                Ohio  |   .2499127   .2896401     0.86   0.388    -.3181142    .8179397
            Oklahoma  |   .1205288   .3272728     0.37   0.713    -.5213013    .7623589
              Oregon  |    .117889   .3111794     0.38   0.705    -.4923796    .7281576
        Pennsylvania  |   .3583187   .2770834     1.29   0.196    -.1850826    .9017201
        Rhode Island  |   .6586832   .4479327     1.47   0.142    -.2197789    1.537145
      South Carolina  |   .1464299   .3405795     0.43   0.667    -.5214966    .8143565
        South Dakota  |   .1181791   .5718212     0.21   0.836    -1.003247    1.239605
           Tennessee  |   .0060005   .3088589     0.02   0.985    -.5997173    .6117183
               Texas  |   .2734127   .2845183     0.96   0.337    -.2845696    .8313951
                Utah  |   .0949245   .3653944     0.26   0.795    -.6216677    .8115167
             Vermont  |   .5497348   .3954583     1.39   0.165    -.2258172    1.325287
            Virginia  |   .4006216   .2919775     1.37   0.170    -.1719894    .9732325
          Washington  |  -.0610762   .3267195    -0.19   0.852    -.7018213    .5796689
       West Virginia  |   .4593035   .3605459     1.27   0.203      -.24778    1.166387
           Wisconsin  |   .0539925   .3077146     0.18   0.861    -.5494812    .6574662
             Wyoming  |  -.7274131   1.529527    -0.48   0.634    -3.727041    2.272215
                      |
                rural |
                   2  |  -.0272517   .0702592    -0.39   0.698    -.1650404     .110537
                   3  |   .1412877    .086195     1.64   0.101    -.0277534    .3103288
                   4  |  -.0498419   .1371405    -0.36   0.716    -.3187947    .2191108
                   5  |    .104244   .1847438     0.56   0.573    -.2580658    .4665538
                   6  |   .1291941   .1196308     1.08   0.280    -.1054194    .3638076
                   7  |   .1954438   .1689691     1.16   0.248    -.1359296    .5268172
                   8  |  -.2874377   .3378726    -0.85   0.395    -.9500556    .3751802
                   9  |  -.1052733   .2676162    -0.39   0.694    -.6301081    .4195615
                      |
                _cons |   3.415568   .3517419     9.71   0.000     2.725751    4.105386
---------------------------------------------------------------------------------------

. esttab sate_w1 sate_w1_c using "tables/S11.csv", b(3) se(3) wide label nobaselevels csv repla
> ce
(output written to tables/S11.csv)

. 
. 
. 
. * CATE estimates: WHITES ONLY
. 
. eststo cates_w1: reg banentry_w1 i.entry_exp_recode##i.democrat##c.racialresentment  i.agecat
>  gender white married i.educ i.income i.inputstate i.rural [pweight=weight], robust
(sum of wgt is 1,959.53164743084)
note: white omitted because of collinearity.

Linear regression                               Number of obs     =      2,093
                                                F(101, 1991)      =       7.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2170
                                                Root MSE          =      1.067

---------------------------------------------------------------------------------------------
                            |               Robust
                banentry_w1 | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
           entry_exp_recode |
                     China  |    .808475   .5091803     1.59   0.112    -.1901071    1.807057
                     Italy  |   .6673353   .5016265     1.33   0.184    -.3164327    1.651103
                            |
                   democrat |
                  Democrat  |   .1803631    .420407     0.43   0.668    -.6441207    1.004847
                     Other  |    .235206   .4445238     0.53   0.597    -.6365746    1.106987
                            |
  entry_exp_recode#democrat |
            China#Democrat  |   -1.59645   .5729026    -2.79   0.005    -2.720001   -.4728984
               China#Other  |  -1.429575   .5924772    -2.41   0.016    -2.591516    -.267635
            Italy#Democrat  |   -.542309   .5527629    -0.98   0.327    -1.626363    .5417453
               Italy#Other  |  -.7810959   .5748516    -1.36   0.174     -1.90847    .3462779
                            |
           racialresentment |   .2162838   .0905631     2.39   0.017     .0386754    .3938921
                            |
           entry_exp_recode#|
         c.racialresentment |
                     China  |  -.0769201   .1167551    -0.66   0.510    -.3058951    .1520549
                     Italy  |  -.0433671   .1134718    -0.38   0.702    -.2659029    .1791688
                            |
democrat#c.racialresentment |
                  Democrat  |  -.0886731   .1090364    -0.81   0.416    -.3025106    .1251643
                     Other  |  -.0706779    .105044    -0.67   0.501    -.2766856    .1353299
                            |
  entry_exp_recode#democrat#|
         c.racialresentment |
            China#Democrat  |   .3272189   .1486007     2.20   0.028     .0357897    .6186481
               China#Other  |   .2747671   .1423249     1.93   0.054    -.0043543    .5538884
            Italy#Democrat  |   .0688887   .1431747     0.48   0.630    -.2118993    .3496766
               Italy#Other  |   .1192567   .1352067     0.88   0.378    -.1459047    .3844182
                            |
                     agecat |
                       30-  |  -.0318218   .0977632    -0.33   0.745    -.2235507     .159907
                       45-  |    .126388   .0929712     1.36   0.174    -.0559431     .308719
                       65-  |   .0388796   .0982553     0.40   0.692    -.1538143    .2315736
                            |
                     gender |   .1077444   .0507498     2.12   0.034     .0082162    .2072727
                      white |          0  (omitted)
                    married |       .015   .0553296     0.27   0.786    -.0935099      .12351
                            |
                       educ |
      High school graduate  |  -.2721166   .1652283    -1.65   0.100    -.5961552    .0519219
              Some college  |  -.4433102    .169613    -2.61   0.009    -.7759478   -.1106726
                    2-year  |   -.365367   .1723381    -2.12   0.034     -.703349   -.0273851
                    4-year  |  -.6191869   .1702339    -3.64   0.000    -.9530421   -.2853316
                 Post-grad  |  -.5804476    .179491    -3.23   0.001    -.9324575   -.2284376
                            |
                     income |
                         2  |   .3177283   .1660825     1.91   0.056    -.0079854     .643442
                         3  |   .1644336   .1543899     1.07   0.287    -.1383491    .4672162
                         4  |   .1302461   .1619945     0.80   0.421    -.1874504    .4479425
                         5  |   .2354392   .1605715     1.47   0.143    -.0794667     .550345
                         6  |   .2782693   .1710923     1.63   0.104    -.0572694    .6138079
                         7  |   .3971749   .1691113     2.35   0.019     .0655211    .7288286
                         8  |   .1736524   .1735149     1.00   0.317    -.1666373    .5139422
                         9  |   .2112074   .1682357     1.26   0.209    -.1187291    .5411438
                        10  |   .1162876   .1770872     0.66   0.511     -.231008    .4635832
                        11  |   .2164556   .1789837     1.21   0.227    -.1345594    .5674705
                        12  |   .1503351   .1969298     0.76   0.445     -.235875    .5365452
                        13  |   .2259823   .2250792     1.00   0.315    -.2154333    .6673978
                        14  |   .1094887   .2505478     0.44   0.662    -.3818746     .600852
                        15  |   .6515454   .1989878     3.27   0.001     .2612992    1.041792
                        16  |   .5945278   .2376206     2.50   0.012     .1285167    1.060539
                        17  |   .2620571    .155842     1.68   0.093    -.0435733    .5676875
                            |
                 inputstate |
                    Alaska  |  -.2572556   .6478825    -0.40   0.691    -1.527854    1.013343
                   Arizona  |   .2342753   .2785714     0.84   0.400    -.3120467    .7805972
                  Arkansas  |   .6044519   .3068207     1.97   0.049     .0027286    1.206175
                California  |   .0529791   .2656034     0.20   0.842    -.4679105    .5738688
                  Colorado  |   .1571627   .3025163     0.52   0.603    -.4361191    .7504445
               Connecticut  |   .6780741   .2811247     2.41   0.016     .1267447    1.229403
                  Delaware  |  -.2183939   .4232495    -0.52   0.606    -1.048452    .6116645
      District of Columbia  |   -.050836   .6771394    -0.08   0.940    -1.378812     1.27714
                   Florida  |   .2617145   .2495485     1.05   0.294     -.227689     .751118
                   Georgia  |   .3851963   .2626372     1.47   0.143    -.1298763    .9002689
                    Hawaii  |   .3333019   .4749306     0.70   0.483    -.5981112    1.264715
                     Idaho  |  -.2540973   .4517039    -0.56   0.574    -1.139959    .6317647
                  Illinois  |    .231892   .2697199     0.86   0.390    -.2970707    .7608548
                   Indiana  |   .2686735   .2848806     0.94   0.346    -.2900218    .8273688
                      Iowa  |   .6094422   .3040197     2.00   0.045     .0132122    1.205672
                    Kansas  |   .5821649   .2857393     2.04   0.042     .0217855    1.142544
                  Kentucky  |    .440881   .2706309     1.63   0.103    -.0898684    .9716304
                 Louisiana  |   .6839911   .2794187     2.45   0.014     .1360074    1.231975
                     Maine  |   .7212864   .3465743     2.08   0.038        .0416    1.400973
                  Maryland  |   .4517205   .3370751     1.34   0.180    -.2093365    1.112777
             Massachusetts  |   .2625504   .2850312     0.92   0.357    -.2964403    .8215412
                  Michigan  |   .2694644    .267025     1.01   0.313    -.2542133    .7931421
                 Minnesota  |   .1113826   .2898844     0.38   0.701    -.4571259    .6798911
               Mississippi  |  -.0179947   .3319046    -0.05   0.957    -.6689114     .632922
                  Missouri  |   .4707104   .2588065     1.82   0.069    -.0368496    .9782705
                   Montana  |  -.6981251   .3854438    -1.81   0.070    -1.454041    .0577903
                  Nebraska  |   .0786822   .4974576     0.16   0.874    -.8969098    1.054274
                    Nevada  |   .2573607   .3209609     0.80   0.423    -.3720938    .8868152
             New Hampshire  |  -.0077795   .3308535    -0.02   0.981    -.6566348    .6410758
                New Jersey  |   .4225471   .2940009     1.44   0.151    -.1540346    .9991287
                New Mexico  |   .3907364   .3084649     1.27   0.205    -.2142114    .9956843
                  New York  |   .2994808   .2563275     1.17   0.243    -.2032175    .8021791
            North Carolina  |   .4043848   .2623575     1.54   0.123    -.1101391    .9189087
              North Dakota  |  -.7774914   .4307069    -1.81   0.071    -1.622175    .0671921
                      Ohio  |   .3476742   .2641801     1.32   0.188    -.1704242    .8657726
                  Oklahoma  |    .066553   .2921073     0.23   0.820    -.5063151     .639421
                    Oregon  |   .3260244    .288431     1.13   0.258    -.2396339    .8916827
              Pennsylvania  |   .4009423   .2507623     1.60   0.110    -.0908418    .8927264
              Rhode Island  |   .7896236   .4265269     1.85   0.064    -.0468622    1.626109
            South Carolina  |   .2477627   .3039935     0.82   0.415     -.348416    .8439414
              South Dakota  |   .0751163   .5144955     0.15   0.884    -.9338898    1.084122
                 Tennessee  |   .0754111   .2778735     0.27   0.786    -.4695422    .6203645
                     Texas  |   .2830742   .2551991     1.11   0.267    -.2174111    .7835594
                      Utah  |    .458492    .424636     1.08   0.280    -.3742855     1.29127
                   Vermont  |    .728677   .3530503     2.06   0.039     .0362901    1.421064
                  Virginia  |   .3785331   .2624366     1.44   0.149    -.1361461    .8932123
                Washington  |   .1335334   .3050337     0.44   0.662    -.4646853    .7317522
             West Virginia  |   .6387748   .3326351     1.92   0.055    -.0135746    1.291124
                 Wisconsin  |   .0826699    .283752     0.29   0.771    -.4738121    .6391519
                   Wyoming  |  -1.119883   1.352309    -0.83   0.408    -3.771973    1.532207
                            |
                      rural |
                         2  |  -.0460913   .0671488    -0.69   0.493    -.1777806     .085598
                         3  |   .1093803   .0811433     1.35   0.178    -.0497544     .268515
                         4  |  -.0689532   .1274213    -0.54   0.588    -.3188463      .18094
                         5  |   .0512577   .1773344     0.29   0.773    -.2965228    .3990382
                         6  |  -.0078277    .112865    -0.07   0.945    -.2291736    .2135183
                         7  |   .0950976   .1643346     0.58   0.563    -.2271883    .4173835
                         8  |  -.5119181   .3224202    -1.59   0.113    -1.144235    .1203984
                         9  |   -.171264   .2499143    -0.69   0.493    -.6613849    .3188569
                            |
                      _cons |   2.783751   .5079505     5.48   0.000     1.787581    3.779922
---------------------------------------------------------------------------------------------

. esttab cates_w1, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                      banentry_w1                
-------------------------------------------------
China                       0.808         (0.509)
Italy                       0.667         (0.502)
Democrat                    0.180         (0.420)
Other                       0.235         (0.445)
China # Democrat           -1.596**       (0.573)
China # Other              -1.430*        (0.592)
Italy # Democrat           -0.542         (0.553)
Italy # Other              -0.781         (0.575)
racialresentment            0.216*        (0.091)
China # racialrese~t       -0.077         (0.117)
Italy # racialrese~t       -0.043         (0.113)
Democrat # racialr~t       -0.089         (0.109)
Other # racialrese~t       -0.071         (0.105)
China # Democrat #~m        0.327*        (0.149)
China # Other # ra~t        0.275         (0.142)
Italy # Democrat #~m        0.069         (0.143)
Italy # Other # ra~t        0.119         (0.135)
30-                        -0.032         (0.098)
45-                         0.126         (0.093)
65-                         0.039         (0.098)
gender                      0.108*        (0.051)
white                       0.000             (.)
married                     0.015         (0.055)
High school graduate       -0.272         (0.165)
Some college               -0.443**       (0.170)
2-year                     -0.365*        (0.172)
4-year                     -0.619***      (0.170)
Post-grad                  -0.580**       (0.179)
income=2                    0.318         (0.166)
income=3                    0.164         (0.154)
income=4                    0.130         (0.162)
income=5                    0.235         (0.161)
income=6                    0.278         (0.171)
income=7                    0.397*        (0.169)
income=8                    0.174         (0.174)
income=9                    0.211         (0.168)
income=10                   0.116         (0.177)
income=11                   0.216         (0.179)
income=12                   0.150         (0.197)
income=13                   0.226         (0.225)
income=14                   0.109         (0.251)
income=15                   0.652**       (0.199)
income=16                   0.595*        (0.238)
income=17                   0.262         (0.156)
Alaska                     -0.257         (0.648)
Arizona                     0.234         (0.279)
Arkansas                    0.604*        (0.307)
California                  0.053         (0.266)
Colorado                    0.157         (0.303)
Connecticut                 0.678*        (0.281)
Delaware                   -0.218         (0.423)
District of Columbia       -0.051         (0.677)
Florida                     0.262         (0.250)
Georgia                     0.385         (0.263)
Hawaii                      0.333         (0.475)
Idaho                      -0.254         (0.452)
Illinois                    0.232         (0.270)
Indiana                     0.269         (0.285)
Iowa                        0.609*        (0.304)
Kansas                      0.582*        (0.286)
Kentucky                    0.441         (0.271)
Louisiana                   0.684*        (0.279)
Maine                       0.721*        (0.347)
Maryland                    0.452         (0.337)
Massachusetts               0.263         (0.285)
Michigan                    0.269         (0.267)
Minnesota                   0.111         (0.290)
Mississippi                -0.018         (0.332)
Missouri                    0.471         (0.259)
Montana                    -0.698         (0.385)
Nebraska                    0.079         (0.497)
Nevada                      0.257         (0.321)
New Hampshire              -0.008         (0.331)
New Jersey                  0.423         (0.294)
New Mexico                  0.391         (0.308)
New York                    0.299         (0.256)
North Carolina              0.404         (0.262)
North Dakota               -0.777         (0.431)
Ohio                        0.348         (0.264)
Oklahoma                    0.067         (0.292)
Oregon                      0.326         (0.288)
Pennsylvania                0.401         (0.251)
Rhode Island                0.790         (0.427)
South Carolina              0.248         (0.304)
South Dakota                0.075         (0.514)
Tennessee                   0.075         (0.278)
Texas                       0.283         (0.255)
Utah                        0.458         (0.425)
Vermont                     0.729*        (0.353)
Virginia                    0.379         (0.262)
Washington                  0.134         (0.305)
West Virginia               0.639         (0.333)
Wisconsin                   0.083         (0.284)
Wyoming                    -1.120         (1.352)
Rural Zip Code Ind~2       -0.046         (0.067)
Rural Zip Code Ind~3        0.109         (0.081)
Rural Zip Code Ind~4       -0.069         (0.127)
Rural Zip Code Ind~5        0.051         (0.177)
Rural Zip Code Ind~6       -0.008         (0.113)
Rural Zip Code Ind~7        0.095         (0.164)
Rural Zip Code Ind~8       -0.512         (0.322)
Rural Zip Code Ind~9       -0.171         (0.250)
Constant                    2.784***      (0.508)
-------------------------------------------------
Observations                 2093                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_w1 using "tables/S12.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S12.csv)

. 
. 
. 
. 
. * FOUR WAVES: WHITES ONLY
. 
. 
. preserve 

. 
. keep banentryexp_w* banentry_w* democrat_w* caseid weight* aia racialresentment agecat gender
>  white married educ income inputstate rural

. drop banentryexp_w1 banentryexp_w2 banentry_w1 banentry_w2

. drop weight

. 
. reshape long banentryexp_w banentry_w democrat_w weight_w, i(caseid) j(wave)
(j = 1 2 3 4 5 6)
(variable banentryexp_w1 not found)
(variable banentry_w1 not found)
(variable banentryexp_w2 not found)
(variable banentry_w2 not found)
weight_w2:  1771 values would be changed; not changed
weight_w3:  1556 values would be changed; not changed
weight_w4:  1457 values would be changed; not changed
weight_w5:  1423 values would be changed; not changed
weight_w6:  1250 values would be changed; not changed

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            2,171   ->   13,026      
Number of variables                  31   ->   16          
j variable (6 values)                     ->   wave
xij variables:
banentryexp_w1 banentryexp_w2 ... banentryexp_w6->banentryexp_w
banentry_w1 banentry_w2 ... banentry_w6   ->   banentry_w
democrat_w1 democrat_w2 ... democrat_w6   ->   democrat_w
      weight_w1 weight_w2 ... weight_w6   ->   weight_w
-----------------------------------------------------------------------------

. label values democrat democrat

. 
. xtset caseid wave

Panel variable: caseid (strongly balanced)
 Time variable: wave, 1 to 6
         Delta: 1 unit

. gen  treatwaveid = wave*10+banentryexp_w
(7,340 missing values generated)

. sort treatwaveid

. merge m:1 treatwaveid using country_data.dta
(variable banentryexp_w was long, now double to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         7,340
        from master                     7,340  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             5,686  (_merge==3)
    -----------------------------------------

. drop if wave==.
(0 observations deleted)

. drop _merge

. 
. 
. graph drop _all

. 
. 
. 
. * SATE Analysis
. eststo sate_w2to5: xtreg banentry_w ib3.banentryexp_w, re robust

Random-effects GLS regression                   Number of obs     =      5,686
Group variable: caseid                          Number of groups  =      1,827

R-squared:                                      Obs per group:
     Within  = 0.0519                                         min =          1
     Between = 0.0121                                         avg =        3.1
     Overall = 0.0276                                         max =          4

                                                Wald chi2(2)      =     209.92
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                              (Std. err. adjusted for 1,827 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
   banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
banentryexp_w |
       China  |   .3526461   .0405054     8.71   0.000      .273257    .4320353
      Brazil  |   .5042064   .0350953    14.37   0.000     .4354208    .5729919
              |
        _cons |    2.96687   .0290577   102.10   0.000     2.909918    3.023822
--------------+----------------------------------------------------------------
      sigma_u |  .81791326
      sigma_e |  .92885474
          rho |  .43674273   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. eststo sate_w2to5_f: xtreg banentry_w ib3.banentryexp_w, fe robust

Fixed-effects (within) regression               Number of obs     =      5,686
Group variable: caseid                          Number of groups  =      1,827

R-squared:                                      Obs per group:
     Within  = 0.0519                                         min =          1
     Between = 0.0120                                         avg =        3.1
     Overall = 0.0275                                         max =          4

                                                F(2,1826)         =      90.45
corr(u_i, Xb) = -0.0095                         Prob > F          =     0.0000

                              (Std. err. adjusted for 1,827 clusters in caseid)
-------------------------------------------------------------------------------
              |               Robust
   banentry_w | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
banentryexp_w |
       China  |   .3539071   .0432781     8.18   0.000     .2690273    .4387869
      Brazil  |   .5175087    .038736    13.36   0.000     .4415372    .5934801
              |
        _cons |   2.974746   .0238171   124.90   0.000     2.928034    3.021457
--------------+----------------------------------------------------------------
      sigma_u |  1.0037304
      sigma_e |  .92885474
          rho |   .5386857   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. eststo sate_w2to5_c: xtreg banentry_w ib3.banentryexp_w i.agecat gender white married i.educ 
> i.income i.inputstate i.rural, re robust
note: white omitted because of collinearity.

Random-effects GLS regression                   Number of obs     =      5,530
Group variable: caseid                          Number of groups  =      1,772

R-squared:                                      Obs per group:
     Within  = 0.0539                                         min =          1
     Between = 0.1650                                         avg =        3.1
     Overall = 0.1278                                         max =          4

                                                Wald chi2(86)     =     745.38
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                      (Std. err. adjusted for 1,772 clusters in caseid)
---------------------------------------------------------------------------------------
                      |               Robust
           banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------+----------------------------------------------------------------
        banentryexp_w |
               China  |   .3704234   .0410037     9.03   0.000     .2900576    .4507891
              Brazil  |   .5113723   .0358134    14.28   0.000     .4411792    .5815653
                      |
               agecat |
                 30-  |   .0680381   .0847867     0.80   0.422    -.0981408    .2342169
                 45-  |   .4915845   .0794946     6.18   0.000      .335778     .647391
                 65-  |   .4131983   .0824228     5.01   0.000     .2516526    .5747441
                      |
               gender |  -.0690093   .0446631    -1.55   0.122    -.1565473    .0185287
                white |          0  (omitted)
              married |   .0954659   .0479949     1.99   0.047     .0013976    .1895342
                      |
                 educ |
High school graduate  |   -.175771   .1308384    -1.34   0.179    -.4322096    .0806676
        Some college  |  -.3671203    .134574    -2.73   0.006    -.6308806     -.10336
              2-year  |  -.3051277   .1433156    -2.13   0.033    -.5860212   -.0242342
              4-year  |  -.5683001   .1367413    -4.16   0.000    -.8363081   -.3002921
           Post-grad  |   -.709451   .1429024    -4.96   0.000    -.9895346   -.4293675
                      |
               income |
                   2  |   .1897643   .1425048     1.33   0.183      -.08954    .4690686
                   3  |   .0031731   .1411749     0.02   0.982    -.2735247    .2798709
                   4  |   -.021521   .1420145    -0.15   0.880    -.2998643    .2568223
                   5  |  -.0492988   .1437287    -0.34   0.732    -.3310018    .2324042
                   6  |  -.0091849    .144945    -0.06   0.949    -.2932719    .2749021
                   7  |  -.0279307   .1571986    -0.18   0.859    -.3360344    .2801729
                   8  |  -.0043996   .1439445    -0.03   0.976    -.2865256    .2777263
                   9  |  -.1359888    .148189    -0.92   0.359     -.426434    .1544563
                  10  |  -.1522452   .1483268    -1.03   0.305    -.4429604      .13847
                  11  |  -.0978845   .1576961    -0.62   0.535    -.4069631    .2111942
                  12  |  -.1673229   .1655124    -1.01   0.312    -.4917213    .1570754
                  13  |  -.0856283   .1859826    -0.46   0.645    -.4501475    .2788909
                  14  |  -.1069325   .2247514    -0.48   0.634    -.5474371    .3335721
                  15  |  -1.213523    .797928    -1.52   0.128    -2.777433    .3503873
                  16  |   .2846149   .2524237     1.13   0.260    -.2101265    .7793563
                  17  |   .0802191   .1379813     0.58   0.561    -.1902192    .3506574
                      |
           inputstate |
              Alaska  |  -.6257193   .3698356    -1.69   0.091    -1.350584    .0991452
             Arizona  |  -.1057401   .2359732    -0.45   0.654    -.5682391    .3567589
            Arkansas  |  -.1125236   .2480903    -0.45   0.650    -.5987716    .3737245
          California  |  -.0465189   .2095081    -0.22   0.824    -.4571473    .3641095
            Colorado  |   .1805099   .2436238     0.74   0.459    -.2969838    .6580037
         Connecticut  |  -.2677706   .3361611    -0.80   0.426    -.9266343     .391093
            Delaware  |   .3269741   .3244496     1.01   0.314    -.3089354    .9628837
District of Columbia  |  -.3788629   .2857014    -1.33   0.185    -.9388273    .1811015
             Florida  |   .2579599   .2082385     1.24   0.215      -.15018    .6660998
             Georgia  |    .197221   .2271261     0.87   0.385    -.2479379      .64238
              Hawaii  |  -.1878922   .4165954    -0.45   0.652    -1.004404    .6286198
               Idaho  |   .4369942   .2600782     1.68   0.093    -.0727498    .9467382
            Illinois  |   .0103957   .2312466     0.04   0.964    -.4428393    .4636307
             Indiana  |  -.0128826   .2437268    -0.05   0.958    -.4905784    .4648132
                Iowa  |   .5260565   .2511916     2.09   0.036       .03373    1.018383
              Kansas  |   .2271784   .3065984     0.74   0.459    -.3737435    .8281003
            Kentucky  |   .0063352   .2460847     0.03   0.979    -.4759819    .4886523
           Louisiana  |   .4921053   .2662463     1.85   0.065    -.0297279    1.013939
               Maine  |   .1132623   .3750074     0.30   0.763    -.6217387    .8482633
            Maryland  |  -.0991997   .3146689    -0.32   0.753    -.7159395      .51754
       Massachusetts  |  -.1539853   .2455761    -0.63   0.531    -.6353055     .327335
            Michigan  |   .0738164    .214069     0.34   0.730    -.3457511    .4933838
           Minnesota  |  -.2337013   .2442844    -0.96   0.339    -.7124899    .2450874
         Mississippi  |   .1880382   .2851903     0.66   0.510    -.3709245    .7470008
            Missouri  |  -.0241396   .2389423    -0.10   0.920     -.492458    .4441787
             Montana  |  -.3543595   .3936282    -0.90   0.368    -1.125857    .4171375
            Nebraska  |   .0688246   .2762367     0.25   0.803    -.4725894    .6102386
              Nevada  |    .085712   .2874162     0.30   0.766    -.4776133    .6490373
       New Hampshire  |   .2128506   .2873601     0.74   0.459    -.3503649    .7760661
          New Jersey  |   .1474469   .2329561     0.63   0.527    -.3091388    .6040325
          New Mexico  |  -.2419831   .3181511    -0.76   0.447    -.8655479    .3815816
            New York  |   .2218199   .2072952     1.07   0.285    -.1844712     .628111
      North Carolina  |    .056045   .2327939     0.24   0.810    -.4002225    .5123126
        North Dakota  |  -.6202848    .355115    -1.75   0.081    -1.316297    .0757278
                Ohio  |   -.059625   .2145552    -0.28   0.781    -.4801455    .3608955
            Oklahoma  |   .2295677   .2515888     0.91   0.362    -.2635373    .7226727
              Oregon  |   .0348172   .2432792     0.14   0.886    -.4420012    .5116356
        Pennsylvania  |   .1926002   .2060839     0.93   0.350    -.2113169    .5965173
        Rhode Island  |   .0184942   .4496352     0.04   0.967    -.8627745    .8997629
      South Carolina  |  -.1617137   .2781993    -0.58   0.561    -.7069743    .3835469
        South Dakota  |  -.0337093   .3956143    -0.09   0.932    -.8090992    .7416805
           Tennessee  |   .3376473    .229548     1.47   0.141    -.1122585    .7875531
               Texas  |   .3219787   .2154932     1.49   0.135    -.1003803    .7443376
                Utah  |   .0856544    .261613     0.33   0.743    -.4270977    .5984065
             Vermont  |  -.1134552   .3261372    -0.35   0.728    -.7526725     .525762
            Virginia  |   .0334694   .2359943     0.14   0.887    -.4290709    .4960097
          Washington  |  -.2010115   .2249795    -0.89   0.372    -.6419633    .2399402
       West Virginia  |   .2246817   .2874842     0.78   0.434     -.338777    .7881404
           Wisconsin  |   .0719827   .2247284     0.32   0.749    -.3684769    .5124422
             Wyoming  |  -.0742104   .2426629    -0.31   0.760    -.5498209    .4014001
                      |
                rural |
                   2  |  -.0358423    .061469    -0.58   0.560    -.1563193    .0846347
                   3  |   .1598839    .068459     2.34   0.020     .0257067    .2940612
                   4  |   .1181485   .0898611     1.31   0.189     -.057976     .294273
                   5  |   .2227245   .1236764     1.80   0.072    -.0196767    .4651258
                   6  |   .0874162   .1050637     0.83   0.405    -.1185049    .2933373
                   7  |   .1402417   .1285713     1.09   0.275    -.1117535    .3922369
                   8  |   .1845881   .2515414     0.73   0.463    -.3084241    .6776003
                   9  |   .1718851   .2686099     0.64   0.522    -.3545807    .6983508
                      |
                _cons |   3.006257   .2695803    11.15   0.000      2.47789    3.534625
----------------------+----------------------------------------------------------------
              sigma_u |  .73337023
              sigma_e |  .92910916
                  rho |  .38387068   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

. esttab sate_w2to5 sate_w2to5_c using "tables/S13.csv", b(3) se(3) wide label nobaselevels csv
>  replace
(output written to tables/S13.csv)

. 
. * CATE estimates by wave
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs              13,026
25%            2              1       Sum of wgt.      13,026

50%            3                      Mean           3.282819
                        Largest       Std. dev.       1.45098
75%            5              5
90%            5              5       Variance       2.105343
95%            5              5       Skewness      -.3005258
99%            5              5       Kurtosis       1.757661

. local race_median = r(p50)

. 
. replace wave = wave-1
(13,026 real changes made)

. 
. eststo cates_w2to5: xtreg banentry_w i.banentryexp_w##i.wave##i.democrat_w##c.racialresentmen
> t  i.agecat gender white married i.educ i.income i.inputstate i.rural, re robust
note: white omitted because of collinearity.

Random-effects GLS regression                   Number of obs     =      5,326
Group variable: caseid                          Number of groups  =      1,719

R-squared:                                      Obs per group:
     Within  = 0.2084                                         min =          1
     Between = 0.3546                                         avg =        3.1
     Overall = 0.2994                                         max =          4

                                                Wald chi2(155)    =    2603.56
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                          (Std. err. adjusted for 1,719 clusters in caseid)
-------------------------------------------------------------------------------------------
                          |               Robust
               banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
            banentryexp_w |
                  Brazil  |   .1669575   .5578331     0.30   0.765    -.9263754     1.26029
           Great Britain  |  -.2692045   .5876687    -0.46   0.647    -1.421014     .882605
                          |
                     wave |
                       3  |   .2374479   .5129235     0.46   0.643    -.7678636    1.242759
                       4  |  -.4277176   .5495917    -0.78   0.436    -1.504897    .6494624
                       5  |  -.6464112   .8274094    -0.78   0.435    -2.268104    .9752815
                          |
       banentryexp_w#wave |
                Brazil#3  |  -.4658115    .687194    -0.68   0.498    -1.812687    .8810639
                Brazil#4  |   .1467197    .693092     0.21   0.832    -1.211716    1.505155
                Brazil#5  |   .0241304   .9733109     0.02   0.980    -1.883524    1.931785
         Great Britain#3  |  -.7919607   .6463274    -1.23   0.220    -2.058739    .4748177
         Great Britain#4  |   .0545463   .7446915     0.07   0.942    -1.405022    1.514115
         Great Britain#5  |  -.0523628   1.084694    -0.05   0.961    -2.178325    2.073599
                          |
               democrat_w |
                Democrat  |  -1.364745   .5147932    -2.65   0.008    -2.373721   -.3557683
                   Other  |  -1.628108   .5237912    -3.11   0.002     -2.65472   -.6014958
                          |
 banentryexp_w#democrat_w |
         Brazil#Democrat  |   1.105202   .6092968     1.81   0.070    -.0889978    2.299402
            Brazil#Other  |   1.230208   .6345639     1.94   0.053    -.0135145     2.47393
  Great Britain#Democrat  |   1.044019   .6337568     1.65   0.099    -.1981216     2.28616
     Great Britain#Other  |    1.75308   .6458669     2.71   0.007     .4872044    3.018956
                          |
          wave#democrat_w |
              3#Democrat  |   .2684953   .5573326     0.48   0.630    -.8238564    1.360847
                 3#Other  |   .5475616    .577014     0.95   0.343    -.5833651    1.678488
              4#Democrat  |   .5278357   .6068541     0.87   0.384    -.6615764    1.717248
                 4#Other  |   .3231605   .6339066     0.51   0.610    -.9192737    1.565595
              5#Democrat  |   .3414627   .8740082     0.39   0.696    -1.371562    2.054487
                 5#Other  |   .0340302     .89227     0.04   0.970    -1.714787    1.782847
                          |
       banentryexp_w#wave#|
               democrat_w |
       Brazil#3#Democrat  |   .3888059   .7558052     0.51   0.607    -1.092545    1.870157
          Brazil#3#Other  |   .1102805   .8008583     0.14   0.890    -1.459373    1.679934
       Brazil#4#Democrat  |  -.8003422   .7798544    -1.03   0.305    -2.328829    .7281443
          Brazil#4#Other  |  -.2585377   .8308428    -0.31   0.756     -1.88696    1.369884
       Brazil#5#Democrat  |   .5121628   1.041402     0.49   0.623    -1.528948    2.553274
          Brazil#5#Other  |   .4326909   1.079795     0.40   0.689    -1.683669     2.54905
Great Britain#3#Democrat  |   .3916161   .7170182     0.55   0.585    -1.013714    1.796946
   Great Britain#3#Other  |   .0030904   .7518341     0.00   0.997    -1.470477    1.476658
Great Britain#4#Democrat  |  -.3192184   .8230581    -0.39   0.698    -1.932383    1.293946
   Great Britain#4#Other  |  -.3084262   .8684268    -0.36   0.722    -2.010511    1.393659
Great Britain#5#Democrat  |   .3753382    1.14633     0.33   0.743    -1.871428    2.622104
   Great Britain#5#Other  |   .8829166   1.191409     0.74   0.459    -1.452202    3.218035
                          |
         racialresentment |   .2635864    .107647     2.45   0.014     .0526021    .4745707
                          |
            banentryexp_w#|
       c.racialresentment |
                  Brazil  |  -.0762297   .1238901    -0.62   0.538    -.3190499    .1665905
           Great Britain  |  -.1541753   .1322296    -1.17   0.244    -.4133405    .1049898
                          |
  wave#c.racialresentment |
                       3  |  -.0298367   .1140285    -0.26   0.794    -.2533284     .193655
                       4  |   .0124075    .122555     0.10   0.919    -.2277958    .2526109
                       5  |   .0207669   .1872325     0.11   0.912    -.3462021    .3877359
                          |
       banentryexp_w#wave#|
       c.racialresentment |
                Brazil#3  |   .0602914   .1516239     0.40   0.691     -.236886    .3574689
                Brazil#4  |  -.0931106   .1571256    -0.59   0.553    -.4010712      .21485
                Brazil#5  |  -.0028817   .2187972    -0.01   0.989    -.4317163    .4259529
         Great Britain#3  |   .1880135   .1476011     1.27   0.203    -.1012793    .4773064
         Great Britain#4  |   -.026762   .1688121    -0.16   0.874    -.3576275    .3041036
         Great Britain#5  |    .057368   .2463679     0.23   0.816    -.4255042    .5402403
                          |
               democrat_w#|
       c.racialresentment |
                Democrat  |   .2547931   .1257414     2.03   0.043     .0083444    .5012418
                   Other  |   .2929832   .1192013     2.46   0.014     .0593528    .5266135
                          |
 banentryexp_w#democrat_w#|
       c.racialresentment |
         Brazil#Democrat  |  -.2529999   .1520113    -1.66   0.096    -.5509365    .0449368
            Brazil#Other  |  -.2906735   .1463131    -1.99   0.047    -.5774418   -.0039052
  Great Britain#Democrat  |  -.2166738   .1596318    -1.36   0.175    -.5295465    .0961988
     Great Britain#Other  |  -.3726498   .1503855    -2.48   0.013    -.6674001   -.0778996
                          |
          wave#democrat_w#|
       c.racialresentment |
              3#Democrat  |  -.0888487   .1387121    -0.64   0.522    -.3607194     .183022
                 3#Other  |  -.1026444   .1312111    -0.78   0.434    -.3598134    .1545246
              4#Democrat  |  -.2469745   .1560029    -1.58   0.113    -.5527346    .0587855
                 4#Other  |  -.0793192   .1497878    -0.53   0.596    -.3728978    .2142594
              5#Democrat  |  -.1781543   .2116831    -0.84   0.400    -.5930456     .236737
                 5#Other  |  -.0128499   .2068917    -0.06   0.950    -.4183501    .3926503
                          |
       banentryexp_w#wave#|
               democrat_w#|
       c.racialresentment |
       Brazil#3#Democrat  |  -.0404959   .1883617    -0.21   0.830    -.4096781    .3286863
          Brazil#3#Other  |  -.0076019   .1849694    -0.04   0.967    -.3701352    .3549314
       Brazil#4#Democrat  |   .3446358   .2039441     1.69   0.091    -.0550873    .7443589
          Brazil#4#Other  |    .101421   .1998454     0.51   0.612    -.2902688    .4931108
       Brazil#5#Democrat  |   .0244027   .2553572     0.10   0.924    -.4760882    .5248937
          Brazil#5#Other  |   -.032234   .2506306    -0.13   0.898    -.5234609    .4589929
Great Britain#3#Democrat  |  -.0414083   .1867359    -0.22   0.825    -.4074038    .3245873
   Great Britain#3#Other  |   .0025498   .1780295     0.01   0.989    -.3463816    .3514813
Great Britain#4#Democrat  |   .2214035   .2148972     1.03   0.303    -.1997873    .6425943
   Great Britain#4#Other  |   .1370514   .2082456     0.66   0.510    -.2711024    .5452052
Great Britain#5#Democrat  |   .1007887   .2791991     0.36   0.718    -.4464314    .6480089
   Great Britain#5#Other  |  -.1995359   .2799202    -0.71   0.476    -.7481695    .3490976
                          |
                   agecat |
                     30-  |   .0044518   .0785197     0.06   0.955    -.1494439    .1583475
                     45-  |   .2973139   .0756609     3.93   0.000     .1490213    .4456064
                     65-  |   .2397918   .0781927     3.07   0.002      .086537    .3930467
                          |
                   gender |  -.0625312   .0405675    -1.54   0.123     -.142042    .0169796
                    white |          0  (omitted)
                  married |   .0309425   .0437183     0.71   0.479    -.0547438    .1166288
                          |
                     educ |
    High school graduate  |  -.1043862    .126604    -0.82   0.410    -.3525255     .143753
            Some college  |  -.2324593   .1290433    -1.80   0.072    -.4853794    .0204608
                  2-year  |  -.1851011   .1358334    -1.36   0.173    -.4513297    .0811274
                  4-year  |  -.3729326   .1312541    -2.84   0.004     -.630186   -.1156792
               Post-grad  |  -.3569708   .1377294    -2.59   0.010    -.6269155   -.0870262
                          |
                   income |
                       2  |   .1691782    .126195     1.34   0.180    -.0781594    .4165157
                       3  |   .0218221   .1260697     0.17   0.863      -.22527    .2689142
                       4  |   .0078379   .1220293     0.06   0.949    -.2313351    .2470109
                       5  |   -.097755   .1261482    -0.77   0.438     -.345001     .149491
                       6  |  -.0089773    .127808    -0.07   0.944    -.2594765    .2415218
                       7  |   .0041493   .1437357     0.03   0.977    -.2775674     .285866
                       8  |  -.0557384   .1302355    -0.43   0.669    -.3109953    .1995185
                       9  |  -.1268871   .1327879    -0.96   0.339    -.3871465    .1333723
                      10  |  -.0993877   .1312081    -0.76   0.449    -.3565509    .1577755
                      11  |   -.031824    .141167    -0.23   0.822    -.3085061    .2448582
                      12  |  -.1780118   .1541287    -1.15   0.248    -.4800986    .1240749
                      13  |  -.0775922   .1666717    -0.47   0.642    -.4042627    .2490783
                      14  |  -.1338488   .2050855    -0.65   0.514     -.535809    .2681115
                      15  |   -.802359   .3103703    -2.59   0.010    -1.410674   -.1940445
                      16  |    .181021   .2206079     0.82   0.412    -.2513625    .6134045
                      17  |   .0092837   .1225483     0.08   0.940    -.2309067     .249474
                          |
               inputstate |
                  Alaska  |  -.6876574   .3237697    -2.12   0.034    -1.322234   -.0530806
                 Arizona  |  -.0105877   .2210471    -0.05   0.962    -.4438322    .4226567
                Arkansas  |  -.0157146   .2350725    -0.07   0.947    -.4764482     .445019
              California  |  -.0054579   .1958764    -0.03   0.978    -.3893685    .3784528
                Colorado  |   .2861721   .2253487     1.27   0.204    -.1555031    .7278474
             Connecticut  |  -.1601444   .2827413    -0.57   0.571    -.7143072    .3940185
                Delaware  |   .3617828    .247802     1.46   0.144    -.1239002    .8474659
    District of Columbia  |  -.2835736    .227828    -1.24   0.213    -.7301082    .1629611
                 Florida  |   .2400488   .1935743     1.24   0.215      -.13935    .6194475
                 Georgia  |   .0732756   .2031989     0.36   0.718    -.3249868     .471538
                  Hawaii  |   .1600168   .4237874     0.38   0.706    -.6705912    .9906249
                   Idaho  |   .4167117   .2429634     1.72   0.086    -.0594879    .8929113
                Illinois  |   .1225195   .2139881     0.57   0.567    -.2968896    .5419285
                 Indiana  |  -.0093911   .2222534    -0.04   0.966    -.4449998    .4262176
                    Iowa  |   .6825534   .2457101     2.78   0.005     .2009704    1.164136
                  Kansas  |    .170295   .2564058     0.66   0.507    -.3322512    .6728412
                Kentucky  |   .0148846   .2346733     0.06   0.949    -.4450667    .4748359
               Louisiana  |   .1954631   .2595437     0.75   0.451    -.3132331    .7041594
                   Maine  |   .3066581   .3249511     0.94   0.345    -.3302344    .9435506
                Maryland  |  -.0392758   .2743729    -0.14   0.886    -.5770368    .4984852
           Massachusetts  |  -.0942639   .2201395    -0.43   0.669    -.5257294    .3372016
                Michigan  |   .0568393   .1999716     0.28   0.776    -.3350978    .4487764
               Minnesota  |    -.24016   .2363338    -1.02   0.310    -.7033657    .2230458
             Mississippi  |  -.0247418   .2712237    -0.09   0.927    -.5563306    .5068469
                Missouri  |   .0465148   .2156128     0.22   0.829    -.3760785     .469108
                 Montana  |  -.3442704   .3711922    -0.93   0.354    -1.071794    .3832529
                Nebraska  |    .094709   .3215193     0.29   0.768    -.5354572    .7248751
                  Nevada  |   .1079779   .2612577     0.41   0.679    -.4040777    .6200335
           New Hampshire  |   .1732677   .2344984     0.74   0.460    -.2863406    .6328761
              New Jersey  |   .1346014   .2174868     0.62   0.536    -.2916649    .5608677
              New Mexico  |  -.1949189   .2994426    -0.65   0.515    -.7818157    .3919778
                New York  |   .2181935   .1948604     1.12   0.263    -.1637258    .6001128
          North Carolina  |   .0853689   .2065251     0.41   0.679    -.3194127    .4901506
            North Dakota  |  -.5526104   .3437783    -1.61   0.108    -1.226404    .1211828
                    Ohio  |   -.055428   .2005591    -0.28   0.782    -.4485167    .3376607
                Oklahoma  |    .109452   .2269834     0.48   0.630    -.3354272    .5543313
                  Oregon  |   .1124317   .2270424     0.50   0.620    -.3325633    .5574267
            Pennsylvania  |   .1859473   .1952185     0.95   0.341     -.196674    .5685687
            Rhode Island  |  -.0326535   .3882191    -0.08   0.933    -.7935491     .728242
          South Carolina  |  -.1208974   .2529693    -0.48   0.633    -.6167081    .3749134
            South Dakota  |   -.082582   .3194612    -0.26   0.796    -.7087143    .5435504
               Tennessee  |   .3387108    .208529     1.62   0.104    -.0699985    .7474202
                   Texas  |   .2784228   .1994349     1.40   0.163    -.1124625    .6693081
                    Utah  |   .2322492   .2492026     0.93   0.351     -.256179    .7206773
                 Vermont  |  -.1353302   .3072511    -0.44   0.660    -.7375312    .4668708
                Virginia  |    -.02364    .214475    -0.11   0.912    -.4440032    .3967232
              Washington  |   -.063481   .2038508    -0.31   0.755    -.4630212    .3360592
           West Virginia  |     .27404   .2611965     1.05   0.294    -.2378956    .7859757
               Wisconsin  |   .1407102   .2085484     0.67   0.500    -.2680372    .5494576
                 Wyoming  |  -.5206452   .2371668    -2.20   0.028    -.9854835   -.0558068
                          |
                    rural |
                       2  |  -.0387387   .0562243    -0.69   0.491    -.1489363    .0714589
                       3  |    .126947   .0620492     2.05   0.041     .0053327    .2485612
                       4  |   .0817381   .0837683     0.98   0.329    -.0824448    .2459211
                       5  |    .222177   .1246004     1.78   0.075    -.0220353    .4663894
                       6  |   .0842525   .0999223     0.84   0.399    -.1115916    .2800966
                       7  |   .0725997   .1188918     0.61   0.541     -.160424    .3056233
                       8  |   .0327414   .2309005     0.14   0.887    -.4198153    .4852981
                       9  |   .1837632   .1862564     0.99   0.324    -.1812927    .5488191
                          |
                    _cons |   3.130157   .5378626     5.82   0.000     2.075965    4.184348
--------------------------+----------------------------------------------------------------
                  sigma_u |  .63065471
                  sigma_e |  .85387043
                      rho |  .35296262   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. esttab cates_w2to5, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                       banentry_w                
-------------------------------------------------
Brazil                      0.167         (0.558)
Great Britain              -0.269         (0.588)
Date=3                      0.237         (0.513)
Date=4                     -0.428         (0.550)
Date=5                     -0.646         (0.827)
Brazil # Date=3            -0.466         (0.687)
Brazil # Date=4             0.147         (0.693)
Brazil # Date=5             0.024         (0.973)
Great Britain # Da~3       -0.792         (0.646)
Great Britain # Da~4        0.055         (0.745)
Great Britain # Da~5       -0.052         (1.085)
Democrat                   -1.365**       (0.515)
Other                      -1.628**       (0.524)
Brazil # Democrat           1.105         (0.609)
Brazil # Other              1.230         (0.635)
Great Britain # De~t        1.044         (0.634)
Great Britain # Ot~r        1.753**       (0.646)
Date=3 # Democrat           0.268         (0.557)
Date=3 # Other              0.548         (0.577)
Date=4 # Democrat           0.528         (0.607)
Date=4 # Other              0.323         (0.634)
Date=5 # Democrat           0.341         (0.874)
Date=5 # Other              0.034         (0.892)
Brazil # Date=3 # ~t        0.389         (0.756)
Brazil # Date=3 # ~r        0.110         (0.801)
Brazil # Date=4 # ~t       -0.800         (0.780)
Brazil # Date=4 # ~r       -0.259         (0.831)
Brazil # Date=5 # ~t        0.512         (1.041)
Brazil # Date=5 # ~r        0.433         (1.080)
Great Britain # Da~a        0.392         (0.717)
Great Britain # Da~r        0.003         (0.752)
Great Britain # Da~a       -0.319         (0.823)
Great Britain # Da~r       -0.308         (0.868)
Great Britain # Da~a        0.375         (1.146)
Great Britain # Da~r        0.883         (1.191)
racialresentment            0.264*        (0.108)
Brazil # racialres~t       -0.076         (0.124)
Great Britain # ra~t       -0.154         (0.132)
Date=3 # racialres~t       -0.030         (0.114)
Date=4 # racialres~t        0.012         (0.123)
Date=5 # racialres~t        0.021         (0.187)
Brazil # Date=3 # ~e        0.060         (0.152)
Brazil # Date=4 # ~e       -0.093         (0.157)
Brazil # Date=5 # ~e       -0.003         (0.219)
Great Britain # Da~r        0.188         (0.148)
Great Britain # Da~r       -0.027         (0.169)
Great Britain # Da~r        0.057         (0.246)
Democrat # racialr~t        0.255*        (0.126)
Other # racialrese~t        0.293*        (0.119)
Brazil # Democrat ~t       -0.253         (0.152)
Brazil # Other # r~n       -0.291*        (0.146)
Great Britain # De~a       -0.217         (0.160)
Great Britain # Ot~e       -0.373*        (0.150)
Date=3 # Democrat ~t       -0.089         (0.139)
Date=3 # Other # r~n       -0.103         (0.131)
Date=4 # Democrat ~t       -0.247         (0.156)
Date=4 # Other # r~n       -0.079         (0.150)
Date=5 # Democrat ~t       -0.178         (0.212)
Date=5 # Other # r~n       -0.013         (0.207)
Brazil # Date=3 # ~c       -0.040         (0.188)
Brazil # Date=3 # ~l       -0.008         (0.185)
Brazil # Date=4 # ~c        0.345         (0.204)
Brazil # Date=4 # ~l        0.101         (0.200)
Brazil # Date=5 # ~c        0.024         (0.255)
Brazil # Date=5 # ~l       -0.032         (0.251)
Great Britain # Da~a       -0.041         (0.187)
Great Britain # Da~#        0.003         (0.178)
Great Britain # Da~a        0.221         (0.215)
Great Britain # Da~#        0.137         (0.208)
Great Britain # Da~a        0.101         (0.279)
Great Britain # Da~#       -0.200         (0.280)
30-                         0.004         (0.079)
45-                         0.297***      (0.076)
65-                         0.240**       (0.078)
gender                     -0.063         (0.041)
white                       0.000             (.)
married                     0.031         (0.044)
High school graduate       -0.104         (0.127)
Some college               -0.232         (0.129)
2-year                     -0.185         (0.136)
4-year                     -0.373**       (0.131)
Post-grad                  -0.357**       (0.138)
income=2                    0.169         (0.126)
income=3                    0.022         (0.126)
income=4                    0.008         (0.122)
income=5                   -0.098         (0.126)
income=6                   -0.009         (0.128)
income=7                    0.004         (0.144)
income=8                   -0.056         (0.130)
income=9                   -0.127         (0.133)
income=10                  -0.099         (0.131)
income=11                  -0.032         (0.141)
income=12                  -0.178         (0.154)
income=13                  -0.078         (0.167)
income=14                  -0.134         (0.205)
income=15                  -0.802**       (0.310)
income=16                   0.181         (0.221)
income=17                   0.009         (0.123)
Alaska                     -0.688*        (0.324)
Arizona                    -0.011         (0.221)
Arkansas                   -0.016         (0.235)
California                 -0.005         (0.196)
Colorado                    0.286         (0.225)
Connecticut                -0.160         (0.283)
Delaware                    0.362         (0.248)
District of Columbia       -0.284         (0.228)
Florida                     0.240         (0.194)
Georgia                     0.073         (0.203)
Hawaii                      0.160         (0.424)
Idaho                       0.417         (0.243)
Illinois                    0.123         (0.214)
Indiana                    -0.009         (0.222)
Iowa                        0.683**       (0.246)
Kansas                      0.170         (0.256)
Kentucky                    0.015         (0.235)
Louisiana                   0.195         (0.260)
Maine                       0.307         (0.325)
Maryland                   -0.039         (0.274)
Massachusetts              -0.094         (0.220)
Michigan                    0.057         (0.200)
Minnesota                  -0.240         (0.236)
Mississippi                -0.025         (0.271)
Missouri                    0.047         (0.216)
Montana                    -0.344         (0.371)
Nebraska                    0.095         (0.322)
Nevada                      0.108         (0.261)
New Hampshire               0.173         (0.234)
New Jersey                  0.135         (0.217)
New Mexico                 -0.195         (0.299)
New York                    0.218         (0.195)
North Carolina              0.085         (0.207)
North Dakota               -0.553         (0.344)
Ohio                       -0.055         (0.201)
Oklahoma                    0.109         (0.227)
Oregon                      0.112         (0.227)
Pennsylvania                0.186         (0.195)
Rhode Island               -0.033         (0.388)
South Carolina             -0.121         (0.253)
South Dakota               -0.083         (0.319)
Tennessee                   0.339         (0.209)
Texas                       0.278         (0.199)
Utah                        0.232         (0.249)
Vermont                    -0.135         (0.307)
Virginia                   -0.024         (0.214)
Washington                 -0.063         (0.204)
West Virginia               0.274         (0.261)
Wisconsin                   0.141         (0.209)
Wyoming                    -0.521*        (0.237)
Rural Zip Code Ind~2       -0.039         (0.056)
Rural Zip Code Ind~3        0.127*        (0.062)
Rural Zip Code Ind~4        0.082         (0.084)
Rural Zip Code Ind~5        0.222         (0.125)
Rural Zip Code Ind~6        0.084         (0.100)
Rural Zip Code Ind~7        0.073         (0.119)
Rural Zip Code Ind~8        0.033         (0.231)
Rural Zip Code Ind~9        0.184         (0.186)
Constant                    3.130***      (0.538)
-------------------------------------------------
Observations                 5326                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_w2to5 using "tables/S14.csv", b(3) se(3) wide label nobaselevels csv replace
(output written to tables/S14.csv)

. 
. restore 

. 
. 
. 
. 
. preserve 

. 
. keep banentryexp_w* banentry_w* democrat_w* caseid weight* aia racialresentment agecat gender
>  white married educ income inputstate rural

. drop banentryexp_w1 banentryexp_w2 banentry_w1 banentry_w2

. drop weight

. 
. reshape long banentryexp_w banentry_w democrat_w weight_w, i(caseid) j(wave)
(j = 1 2 3 4 5 6)
(variable banentryexp_w1 not found)
(variable banentry_w1 not found)
(variable banentryexp_w2 not found)
(variable banentry_w2 not found)
weight_w2:  1771 values would be changed; not changed
weight_w3:  1556 values would be changed; not changed
weight_w4:  1457 values would be changed; not changed
weight_w5:  1423 values would be changed; not changed
weight_w6:  1250 values would be changed; not changed

Data                               Wide   ->   Long
-----------------------------------------------------------------------------
Number of observations            2,171   ->   13,026      
Number of variables                  31   ->   16          
j variable (6 values)                     ->   wave
xij variables:
banentryexp_w1 banentryexp_w2 ... banentryexp_w6->banentryexp_w
banentry_w1 banentry_w2 ... banentry_w6   ->   banentry_w
democrat_w1 democrat_w2 ... democrat_w6   ->   democrat_w
      weight_w1 weight_w2 ... weight_w6   ->   weight_w
-----------------------------------------------------------------------------

. label values democrat democrat

. 
. xtset caseid wave

Panel variable: caseid (strongly balanced)
 Time variable: wave, 1 to 6
         Delta: 1 unit

. gen  treatwaveid = wave*10+banentryexp_w
(7,340 missing values generated)

. sort treatwaveid

. merge m:1 treatwaveid using country_data.dta
(variable banentryexp_w was long, now double to accommodate using data's values)

    Result                      Number of obs
    -----------------------------------------
    Not matched                         7,340
        from master                     7,340  (_merge==1)
        from using                          0  (_merge==2)

    Matched                             5,686  (_merge==3)
    -----------------------------------------

. drop if wave==.
(0 observations deleted)

. drop _merge

. 
. 
. graph drop _all

. 
. 
. tempvar logcases

. gen `logcases' = ln(new_cases_smoothed)
(7,340 missing values generated)

. 
. summarize racialresentment, detail

                      racialresentment
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            1              1       Obs              13,026
25%            2              1       Sum of wgt.      13,026

50%            3                      Mean           3.282819
                        Largest       Std. dev.       1.45098
75%            5              5
90%            5              5       Variance       2.105343
95%            5              5       Skewness      -.3005258
99%            5              5       Kurtosis       1.757661

. local race_median = r(p50)

. 
. 
. eststo cates_cases_w2to5: xtreg banentry_w i.banentryexp_w##i.democrat_w##c.`logcases'##c.rac
> ialresentment  i.agecat gender white married i.educ i.income i.inputstate i.rural, re robust
note: white omitted because of collinearity.

Random-effects GLS regression                   Number of obs     =      5,326
Group variable: caseid                          Number of groups  =      1,719

R-squared:                                      Obs per group:
     Within  = 0.1460                                         min =          1
     Between = 0.3390                                         avg =        3.1
     Overall = 0.2667                                         max =          4

                                                Wald chi2(119)    =    1910.59
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                          (Std. err. adjusted for 1,719 clusters in caseid)
-------------------------------------------------------------------------------------------
                          |               Robust
               banentry_w | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
            banentryexp_w |
                  Brazil  |   3.589564   3.353556     1.07   0.284    -2.983286    10.16241
           Great Britain  |    .149257    1.31221     0.11   0.909    -2.422627    2.721141
                          |
               democrat_w |
                Democrat  |  -1.289359   .6763698    -1.91   0.057    -2.615019    .0363016
                   Other  |  -2.000713   .7004568    -2.86   0.004    -3.373583   -.6278427
                          |
 banentryexp_w#democrat_w |
         Brazil#Democrat  |  -7.882451   3.807988    -2.07   0.038    -15.34597   -.4189329
            Brazil#Other  |  -3.041149   4.144216    -0.73   0.463    -11.16366    5.081365
  Great Britain#Democrat  |   1.693369   1.443702     1.17   0.241    -1.136235    4.522973
     Great Britain#Other  |   2.930266   1.544971     1.90   0.058    -.0978219    5.958353
                          |
                 __000001 |   .1354351   .1795373     0.75   0.451    -.2164516    .4873219
                          |
 banentryexp_w#c.__000001 |
                  Brazil  |  -.4376989   .3669556    -1.19   0.233    -1.156919    .2815208
           Great Britain  |  -.1695936   .2349854    -0.72   0.470    -.6301566    .2909694
                          |
    democrat_w#c.__000001 |
                Democrat  |   .0884634   .1977989     0.45   0.655    -.2992152     .476142
                   Other  |   .2412121    .207636     1.16   0.245    -.1657469    .6481711
                          |
 banentryexp_w#democrat_w#|
               c.__000001 |
         Brazil#Democrat  |   .7906191   .4129027     1.91   0.056    -.0186553    1.599893
            Brazil#Other  |   .2286328   .4501467     0.51   0.612    -.6536386    1.110904
  Great Britain#Democrat  |  -.1343536   .2593891    -0.52   0.604    -.6427468    .3740397
     Great Britain#Other  |  -.3028091   .2748715    -1.10   0.271    -.8415474    .2359292
                          |
         racialresentment |   .2972587   .1402512     2.12   0.034     .0223713     .572146
                          |
            banentryexp_w#|
       c.racialresentment |
                  Brazil  |  -.7873398   .7554154    -1.04   0.297    -2.267927    .6932471
           Great Britain  |   .1903355    .299635     0.64   0.525    -.3969383    .7776093
                          |
               democrat_w#|
       c.racialresentment |
                Democrat  |   .1540815   .1696815     0.91   0.364     -.178488    .4866511
                   Other  |   .3462533   .1625132     2.13   0.033     .0277332    .6647733
                          |
 banentryexp_w#democrat_w#|
       c.racialresentment |
         Brazil#Democrat  |   1.705446   1.031705     1.65   0.098    -.3166583     3.72755
            Brazil#Other  |    .456054    1.00466     0.45   0.650    -1.513043    2.425151
  Great Britain#Democrat  |  -.3203245    .372785    -0.86   0.390     -1.05097    .4103206
     Great Britain#Other  |   -.677103   .3705822    -1.83   0.068    -1.403431    .0492248
                          |
               c.__000001#|
       c.racialresentment |  -.0131963   .0401539    -0.33   0.742    -.0918965     .065504
                          |
 banentryexp_w#c.__000001#|
       c.racialresentment |
                  Brazil  |   .0775975   .0825377     0.94   0.347    -.0841733    .2393684
           Great Britain  |  -.0273845   .0528593    -0.52   0.604    -.1309868    .0762177
                          |
    democrat_w#c.__000001#|
       c.racialresentment |
                Democrat  |  -.0163225   .0507754    -0.32   0.748    -.1158406    .0831955
                   Other  |  -.0430705   .0482816    -0.89   0.372    -.1377008    .0515597
                          |
 banentryexp_w#democrat_w#|
               c.__000001#|
       c.racialresentment |
         Brazil#Democrat  |  -.1656063   .1114434    -1.49   0.137    -.3840314    .0528189
            Brazil#Other  |  -.0364035   .1079297    -0.34   0.736    -.2479419    .1751349
  Great Britain#Democrat  |   .0352771   .0663888     0.53   0.595    -.0948426    .1653967
     Great Britain#Other  |   .0679637   .0649912     1.05   0.296    -.0594167     .195344
                          |
                   agecat |
                     30-  |  -.0061446   .0777876    -0.08   0.937    -.1586054    .1463163
                     45-  |   .2759023   .0750755     3.67   0.000      .128757    .4230476
                     65-  |   .2090054   .0773998     2.70   0.007     .0573045    .3607063
                          |
                   gender |  -.0528063   .0407845    -1.29   0.195    -.1327424    .0271298
                    white |          0  (omitted)
                  married |   .0342912   .0439474     0.78   0.435    -.0518442    .1204266
                          |
                     educ |
    High school graduate  |  -.1183448   .1258254    -0.94   0.347     -.364958    .1282684
            Some college  |  -.2454043   .1281276    -1.92   0.055    -.4965297    .0057211
                  2-year  |  -.2006329   .1358693    -1.48   0.140    -.4669319     .065666
                  4-year  |  -.3898427   .1299773    -3.00   0.003    -.6445936   -.1350918
               Post-grad  |  -.3749555   .1375403    -2.73   0.006    -.6445296   -.1053815
                          |
                   income |
                       2  |   .1393293   .1292595     1.08   0.281    -.1140146    .3926733
                       3  |  -.0094061   .1286136    -0.07   0.942    -.2614841     .242672
                       4  |    -.00899   .1249831    -0.07   0.943    -.2539523    .2359724
                       5  |  -.1174836   .1291246    -0.91   0.363    -.3705633     .135596
                       6  |  -.0470167   .1308185    -0.36   0.719    -.3034162    .2093827
                       7  |  -.0407116   .1465534    -0.28   0.781    -.3279511    .2465279
                       8  |  -.0926083   .1328788    -0.70   0.486    -.3530459    .1678293
                       9  |  -.1517655   .1348165    -1.13   0.260     -.416001      .11247
                      10  |  -.1116904   .1340476    -0.83   0.405    -.3744188     .151038
                      11  |  -.0410275   .1445292    -0.28   0.777    -.3242996    .2422447
                      12  |  -.2035933   .1569945    -1.30   0.195    -.5112968    .1041102
                      13  |  -.1109408   .1741144    -0.64   0.524    -.4521987    .2303171
                      14  |  -.1952816   .2113838    -0.92   0.356    -.6095863    .2190231
                      15  |  -.8409437   .3783737    -2.22   0.026    -1.582542   -.0993448
                      16  |   .1800638   .2388011     0.75   0.451    -.2879778    .6481054
                      17  |  -.0195523   .1257769    -0.16   0.876    -.2660705    .2269658
                          |
               inputstate |
                  Alaska  |  -.6688723   .3145486    -2.13   0.033    -1.285376   -.0523685
                 Arizona  |   .0380981   .2177006     0.18   0.861    -.3885873    .4647835
                Arkansas  |   .0279832   .2352939     0.12   0.905    -.4331844    .4891508
              California  |   .0370421   .1923757     0.19   0.847    -.3400073    .4140915
                Colorado  |    .356406   .2196465     1.62   0.105    -.0740932    .7869051
             Connecticut  |  -.0958492   .2911567    -0.33   0.742    -.6665058    .4748073
                Delaware  |   .3709524   .2540331     1.46   0.144    -.1269434    .8688481
    District of Columbia  |  -.3203938   .2282905    -1.40   0.160    -.7678351    .1270474
                 Florida  |   .2905736   .1901218     1.53   0.126    -.0820582    .6632055
                 Georgia  |   .0956586    .201019     0.48   0.634    -.2983315    .4896487
                  Hawaii  |   .1965231   .4103601     0.48   0.632    -.6077679    1.000814
                   Idaho  |   .4223824   .2278923     1.85   0.064    -.0242784    .8690431
                Illinois  |   .1443773   .2099825     0.69   0.492    -.2671809    .5559355
                 Indiana  |   .0730537   .2186762     0.33   0.738    -.3555437    .5016511
                    Iowa  |   .7075902   .2477245     2.86   0.004     .2220591    1.193121
                  Kansas  |   .2243378    .243053     0.92   0.356    -.2520373     .700713
                Kentucky  |   .0851525   .2345923     0.36   0.717    -.3746399    .5449449
               Louisiana  |   .2748908   .2641058     1.04   0.298    -.2427472    .7925287
                   Maine  |   .2752197   .3330873     0.83   0.409    -.3776194    .9280587
                Maryland  |  -.0195757   .2680687    -0.07   0.942    -.5449807    .5058293
           Massachusetts  |  -.0672881   .2191124    -0.31   0.759    -.4967406    .3621644
                Michigan  |   .0928367   .1956774     0.47   0.635    -.2906841    .4763574
               Minnesota  |  -.2055561   .2328043    -0.88   0.377    -.6618442     .250732
             Mississippi  |  -.0255994   .2668965    -0.10   0.924    -.5487069    .4975081
                Missouri  |   .0747047   .2135313     0.35   0.726    -.3438089    .4932184
                 Montana  |  -.2784975    .368848    -0.76   0.450    -1.001426    .4444312
                Nebraska  |   .0975187   .3334935     0.29   0.770    -.5561165     .751154
                  Nevada  |   .1252668   .2591686     0.48   0.629    -.3826943    .6332279
           New Hampshire  |   .2196316   .2245372     0.98   0.328    -.2204532    .6597165
              New Jersey  |   .1771258    .213243     0.83   0.406    -.2408229    .5950745
              New Mexico  |  -.1799605   .3121303    -0.58   0.564    -.7917245    .4318036
                New York  |   .2756309   .1916062     1.44   0.150    -.0999105    .6511722
          North Carolina  |   .1227486   .2053685     0.60   0.550    -.2797663    .5252636
            North Dakota  |  -.5072963   .3459276    -1.47   0.143    -1.185302    .1707093
                    Ohio  |  -.0357618   .1971678    -0.18   0.856    -.4222035    .3506799
                Oklahoma  |   .1223015    .218865     0.56   0.576     -.306666    .5512691
                  Oregon  |   .1442217   .2236232     0.64   0.519    -.2940718    .5825152
            Pennsylvania  |   .2290546   .1912863     1.20   0.231    -.1458597    .6039688
            Rhode Island  |   .0481822   .3816685     0.13   0.900    -.6998742    .7962387
          South Carolina  |  -.0633347   .2490495    -0.25   0.799    -.5514627    .4247934
            South Dakota  |  -.0445848    .336559    -0.13   0.895    -.7042283    .6150588
               Tennessee  |    .384271   .2057001     1.87   0.062    -.0188938    .7874357
                   Texas  |   .3015127   .1945202     1.55   0.121    -.0797398    .6827652
                    Utah  |   .2641202   .2441321     1.08   0.279      -.21437    .7426104
                 Vermont  |  -.0394954    .305674    -0.13   0.897    -.6386054    .5596147
                Virginia  |   .0169537   .2126623     0.08   0.936    -.3998568    .4337642
              Washington  |  -.0297018   .1997892    -0.15   0.882    -.4212814    .3618779
           West Virginia  |   .3485599   .2513068     1.39   0.165    -.1439924    .8411122
               Wisconsin  |   .1629012   .2055853     0.79   0.428    -.2400386     .565841
                 Wyoming  |  -.7356017   .2245166    -3.28   0.001    -1.175646   -.2955572
                          |
                    rural |
                       2  |   -.053643   .0564885    -0.95   0.342    -.1643584    .0570725
                       3  |   .1049055   .0624006     1.68   0.093    -.0173974    .2272084
                       4  |   .0864002   .0840895     1.03   0.304    -.0784123    .2512127
                       5  |   .2100349   .1242492     1.69   0.091    -.0334889    .4535588
                       6  |   .0410154   .1028305     0.40   0.690    -.1605287    .2425595
                       7  |   .0605714    .117833     0.51   0.607    -.1703771    .2915198
                       8  |   .0358389   .2297215     0.16   0.876    -.4144069    .4860847
                       9  |   .1609324   .1891373     0.85   0.395    -.2097699    .5316346
                          |
                    _cons |   2.620548   .6600792     3.97   0.000     1.326816    3.914279
--------------------------+----------------------------------------------------------------
                  sigma_u |  .62565632
                  sigma_e |  .88274469
                      rho |   .3343736   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. esttab cates_cases_w2to5, b(3) se(3) wide label nobaselevels

-------------------------------------------------
                              (1)                
                       banentry_w                
-------------------------------------------------
Brazil                      3.590         (3.354)
Great Britain               0.149         (1.312)
Democrat                   -1.289         (0.676)
Other                      -2.001**       (0.700)
Brazil # Democrat          -7.882*        (3.808)
Brazil # Other             -3.041         (4.144)
Great Britain # De~t        1.693         (1.444)
Great Britain # Ot~r        2.930         (1.545)
__000001                    0.135         (0.180)
Brazil # __000001          -0.438         (0.367)
Great Britain~000001       -0.170         (0.235)
Democrat # __000001         0.088         (0.198)
Other # __000001            0.241         (0.208)
Brazil # Demo~000001        0.791         (0.413)
Brazil # Othe~000001        0.229         (0.450)
Great Britain # ~000       -0.134         (0.259)
Great Britain~000001       -0.303         (0.275)
racialresentment            0.297*        (0.140)
Brazil # racialres~t       -0.787         (0.755)
Great Britain # ra~t        0.190         (0.300)
Democrat # racialr~t        0.154         (0.170)
Other # racialrese~t        0.346*        (0.163)
Brazil # Democrat ~t        1.705         (1.032)
Brazil # Other # r~n        0.456         (1.005)
Great Britain # De~a       -0.320         (0.373)
Great Britain # Ot~e       -0.677         (0.371)
__000001 # racialr~t       -0.013         (0.040)
Brazil # __000001 ~t        0.078         (0.083)
Great Britain # __~a       -0.027         (0.053)
Democrat # __00000~e       -0.016         (0.051)
Other # __000001 #~m       -0.043         (0.048)
Brazil # Democrat ~r       -0.166         (0.111)
Brazil # Other # _~i       -0.036         (0.108)
Great Britain # ~000        0.035         (0.066)
Great Britain~000001        0.068         (0.065)
30-                        -0.006         (0.078)
45-                         0.276***      (0.075)
65-                         0.209**       (0.077)
gender                     -0.053         (0.041)
white                       0.000             (.)
married                     0.034         (0.044)
High school graduate       -0.118         (0.126)
Some college               -0.245         (0.128)
2-year                     -0.201         (0.136)
4-year                     -0.390**       (0.130)
Post-grad                  -0.375**       (0.138)
income=2                    0.139         (0.129)
income=3                   -0.009         (0.129)
income=4                   -0.009         (0.125)
income=5                   -0.117         (0.129)
income=6                   -0.047         (0.131)
income=7                   -0.041         (0.147)
income=8                   -0.093         (0.133)
income=9                   -0.152         (0.135)
income=10                  -0.112         (0.134)
income=11                  -0.041         (0.145)
income=12                  -0.204         (0.157)
income=13                  -0.111         (0.174)
income=14                  -0.195         (0.211)
income=15                  -0.841*        (0.378)
income=16                   0.180         (0.239)
income=17                  -0.020         (0.126)
Alaska                     -0.669*        (0.315)
Arizona                     0.038         (0.218)
Arkansas                    0.028         (0.235)
California                  0.037         (0.192)
Colorado                    0.356         (0.220)
Connecticut                -0.096         (0.291)
Delaware                    0.371         (0.254)
District of Columbia       -0.320         (0.228)
Florida                     0.291         (0.190)
Georgia                     0.096         (0.201)
Hawaii                      0.197         (0.410)
Idaho                       0.422         (0.228)
Illinois                    0.144         (0.210)
Indiana                     0.073         (0.219)
Iowa                        0.708**       (0.248)
Kansas                      0.224         (0.243)
Kentucky                    0.085         (0.235)
Louisiana                   0.275         (0.264)
Maine                       0.275         (0.333)
Maryland                   -0.020         (0.268)
Massachusetts              -0.067         (0.219)
Michigan                    0.093         (0.196)
Minnesota                  -0.206         (0.233)
Mississippi                -0.026         (0.267)
Missouri                    0.075         (0.214)
Montana                    -0.278         (0.369)
Nebraska                    0.098         (0.333)
Nevada                      0.125         (0.259)
New Hampshire               0.220         (0.225)
New Jersey                  0.177         (0.213)
New Mexico                 -0.180         (0.312)
New York                    0.276         (0.192)
North Carolina              0.123         (0.205)
North Dakota               -0.507         (0.346)
Ohio                       -0.036         (0.197)
Oklahoma                    0.122         (0.219)
Oregon                      0.144         (0.224)
Pennsylvania                0.229         (0.191)
Rhode Island                0.048         (0.382)
South Carolina             -0.063         (0.249)
South Dakota               -0.045         (0.337)
Tennessee                   0.384         (0.206)
Texas                       0.302         (0.195)
Utah                        0.264         (0.244)
Vermont                    -0.039         (0.306)
Virginia                    0.017         (0.213)
Washington                 -0.030         (0.200)
West Virginia               0.349         (0.251)
Wisconsin                   0.163         (0.206)
Wyoming                    -0.736**       (0.225)
Rural Zip Code Ind~2       -0.054         (0.056)
Rural Zip Code Ind~3        0.105         (0.062)
Rural Zip Code Ind~4        0.086         (0.084)
Rural Zip Code Ind~5        0.210         (0.124)
Rural Zip Code Ind~6        0.041         (0.103)
Rural Zip Code Ind~7        0.061         (0.118)
Rural Zip Code Ind~8        0.036         (0.230)
Rural Zip Code Ind~9        0.161         (0.189)
Constant                    2.621***      (0.660)
-------------------------------------------------
Observations                 5326                
-------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab cates_cases_w2to5 using "tables/S15.csv", b(3) se(3) wide label nobaselevels csv repla
> ce
(output written to tables/S15.csv)

. 
. 
. 
. restore

. 
. 
. 
. * clean up
. rm country_data.dta

. 
. 
. log close
      name:  <unnamed>
       log:  /Users/tp253/Dropbox/Papers/Coronavirus/travel ban/replication/replication_log.log
  log type:  text
 closed on:  21 Apr 2023, 11:51:25
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